Nowadays entropy encoders are part of almost all data compression methods, with the Asymmetrical Numeral Systems (ANS) family of entropy encoders having recently risen in popularity. Entropy encoders based on thetabledvariant of ANS are known to provide varying performances depending on their internal design. In this paper, we present a method that calculates encoder redundancies in almost linear time, which translates in practice to thousand-fold speedups in redundancy calculations for small automatons, and allows redundancy calculations for automatons with tens of millions of states that would be otherwise prohibitive. We also address the problem of improving tabled ANS encoder designs, by employing the aforementioned redundancy calculation method in conjunction with a stochastic hill climbing strategy. The proposed approach consistently outperforms state-of-the-art methods in tabled ANS encoder design. For automatons of twice the alphabet size, experimental results show redundancy reductions around 10% over the default initialization method and over 30% for random initialization.

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Regression wavelet analysis (RWA) is one of the current state-of-the-art lossless compression techniques for remote sensing data. This article presents the first regression-based near-lossless compression method. It is built upon RWA, a quantizer, and a feedback loop to compensate the quantization error. Our near-lossless RWA (NLRWA) proposal can be followed by any entropy coding technique. Here, the NLRWA is coupled with a bitplane-based coder that supports progressive decoding. This successfully enables gradual quality refinement and lossless and near-lossless recovery. A smart strategy for selecting the NLRWA quantization steps is also included. Experimental results show that the proposed scheme outperforms the state-of-the-art lossless and the near-lossless compression methods in terms of compression ratios and quality retrieval.

Recently, Regression Wavelet Analysis (RWA) was proposed as a method for lossless compression of hyperspectral images. In RWA, a linear regression is performed after a spectral wavelet transform to generate predictors which estimate the detail coefficients from approximation coefficients at each scale of the spectral wavelet transform. In this work, we propose Clustering Regression Wavelet Analysis (RWA-C), an extension of the original 'Restricted' RWA model which may be used to improve compression performance while maintaining component scalability. We demonstrate that clustering may be used to group pixels with similar spectral profiles. These clusters may then be more efficiently processed to improve RWA prediction performance while only requiring a modest increase side-information and computational complexity.

We propose a compression encoding method to perceptually optimize the image quality based on a novel quality metric, which emulates how the human visual system form opinion of a compressed image. Compared to the existing perceptual-optimized compression methods, which usually aim to minimize the detectability of compression artifacts and are sub-optimal in visually lossless regime, the proposed encoder aims to operate in the visually lossy regime. We implement the proposed encoder within the JPEG 2000 standard, and demonstrate its advantage over both detectability-based and conventional MSE encoders.

The use of whole-slide images (WSIs) in pathology entails stringent storage and transmission requirements because of their huge dimensions. Therefore, image compression is an essential tool to enable efficient access to these data. In particular, color transforms are needed to exploit the very high degree of inter-component correlation and obtain competitive compression performance. Even though the state-of-the-art color transforms remove some redundancy, they disregard important details of the compression algorithm applied after the transform. Therefore, their coding performance is not optimal. We propose an optimization method called mosaic optimization for designing irreversible and reversible color transforms simultaneously optimized for any given WSI and the subsequent compression algorithm. Mosaic optimization is designed to attain reasonable computational complexity and enable continuous scanner operation. Exhaustive experimental results indicate that, for JPEG 2000 at identical compression ratios, the optimized transforms yield images more similar to the original than the other state-of-the-art transforms. Specifically, irreversible optimized transforms outperform the Karhunen-Loève Transform in terms of PSNR (up to 1.1 dB), the HDR-VDP-2 visual distortion metric (up to 3.8 dB), and the accuracy of computer-aided nuclei detection tasks (F1 score up to 0.04 higher). In addition, reversible optimized transforms achieve PSNR, HDR-VDP-2, and nuclei detection accuracy gains of up to 0.9 dB, 7.1 dB, and 0.025, respectively, when compared with the reversible color transform in lossy-to-lossless compression regimes.

In this work we introduce a lossless compression framework which incorporates convolutional neural networks (CNN) for wavelet subband prediction. A CNN is trained to predict detail coefficients from corresponding approximation coefficients, prediction error is then coded in place of wavelet coefficients. At decompression an identical CNN is used to reproduce the prediction and combine with the decoded residuals for perfect reconstruction of wavelet subbands.

Traditional image compression methods primarily focus on maximizing the fidelity of the compressed image using image quality driven distortion metrics, which are ideally suited for human observers but are not necessarily optimal for machine observers, i.e., automated image exploitation algorithms. For machine observers, task-based distortion metrics, such as probability of error, have been shown to be more effective for tasks such as object detection and classification. This motivates an approach to a task-based image compression, within the JPEG 2000 framework, which preserves the information that is most relevant for the given task. Our proposed method produces a JPEG 2000 compliant compressed codestream, which can be decoded by any JPEG 2000 compliant decoder. We demonstrate the feasibility and the effectiveness of our task-based image compression approach on a simple object classification and detection problem and quantify its performance relative to a conventional MSE encoder.

In this paper, we propose a visual discrimation model to enable perceptually optimized JPEG200 compression for both near-threshold and supra-threshold regimes. The performance of the proposed approach is validated by comparing it to the conventional Mean Squared Error (MSE)-optimized compression.

The conventional strategy to download images captured by satellites is to compress the data on board and then transmit them via the downlink. It often happens that the capacity of the downlink is too small to accommodate all the acquired data, so the images are trimmed and/or transmitted through lossy regimes. This paper introduces a coding system that increases the amount and quality of the downloaded imaging data. The main insight of this paper is to use both the uplink and the downlink to code the images. The uplink is employed to send reference information to the satellite so that the onboard coding system can achieve higher efficiency. This reference information is computed on the ground, possibly employing extensive data and computational resources. The proposed system is called dual link image coding. As it is devised in this paper, it is suitable for Earth observation satellites with polar orbits. Experimental results obtained for data sets acquired by the Landsat 8 satellite indicate significant coding gains with respect to conventional methods.

Digital cameras have become ubiquitous for amateur and professional applications. The raw images captured by digital sensors typically take the form of color filter array (CFA) mosaic images which must be “developed” (via digital signal processing) before they can be viewed. Photographers and scientists often repeat the “development process” using different parameters to obtain images suitable for different purposes. Since the development process is generally not invertible it is commonly desirable to store the raw (or undeveloped) mosaic images indefinitely. Uncompressed mosaic image file sizes can be more than 30 times larger than those of developed images stored in JPEG format. Thus data compression is of interest. Several compression methods for mosaic images have been proposed in the literature. However they all require a custom decompressor followed by development-specific software to generate a displayable image. In this paper a novel compression pipeline that removes these requirements is proposed. Specifically mosaic images can be losslessly recovered from the resulting compressed files and more significantly images can be directly viewed (decompressed and developed) using only a JPEG 2000 compliant image viewer. Experiments reveal that the proposed pipeline attains excellent visual quality while providing compression performance competitive to that of state-of-the-art compression algorithms for mosaic images.

Fast and efficient coding techniques are being increasingly required to meet the complexity restrictions of on-board satellite compression. The recently proposed Regression Wavelet Analysis (RWA) has proven to be highly effective as a spectral transform for coding remote sensing images. The algorithm is based on a pyramidal prediction, using multiple regression analysis, to tackle residual data dependencies in the wavelet domain. RWA combines low complexity and reversibility and has demonstrated competitive performance for lossless and progressive lossy-to-lossless compression superior to the state-of-the-art predictive-based CCSDS-123.0 and the widely used transform-based principal component analysis (PCA). In this paper we introduce a very low-complexity RWA approach, where prediction is based on only a few components, while the performance is maintained. When RWA computational complexity is taken to an extremely low level, careful model selection is necessary. Contrary to expensive selection procedures, we propose a simple and efficient strategy called neighbor selection for using small regression models. On a set of well-known and representative hyperspectral images, these small models maintain the excellent coding performance of RWA, while reducing the computational cost by about 90%.

Image compression systems that exploit the properties of the Human Visual System (HVS) have been studied extensively over the few decades. For the JPEG2000 image compressionstandard, several methods to optimize perceptual quality have been proposed. In 2013, Han et al. proposed a visually lossless compression approach based on the irreversible pipeline defined in the JPEG2000 standard. In this approach, visibility thresholds were measured using psychovisual experiments. These thresholds were then incorporated in a JPEG2000 encoder to ensure that quantization distortions remain below visible levels in the compressed codestreams. In this work, we investigate the use of a similar approach for the reversible pipeline of the JPEG2000 standard. Our motivation is to allow the creation of a scalable codestream that can provide both visually lossless and numerically lossless representations from a single codestream. By comparing the difference in compression performance between the reversible and irreversible pipelines, we also quantify the overhead associated with the reversible pipeline for visually lossless compression.

The Consultative Committee for Space Data Systems (CCSDS) has issued several data compression standards devised to reduce the amount of data transmitted from satellites to ground stations. This paper introduces a contextual arithmetic encoder for on-board data compression. The proposed arithmetic encoder checks the causal adjacent neighbors, at most, to form the context and uses only bitwise operations to estimate the related probabilities. As a result, the encoder consumes few computational resources, making it suitable for on-board operation. Our coding approach is based on the prediction and mapping stages of CCSDS-123 lossless compression standard, an optional quantizer stage to yield lossless or near-lossless compression and our proposed arithmetic encoder. For both lossless and near-lossless compression, the achieved coding performance is superior to that of CCSDS-123, M-CALIC, and JPEG-LS. Taking into account only the entropy encoders, fixed-length codeword is slightly better than MQ and interleaved entropy coding.

Regression Wavelet Analysis (RWA) is a novel wavelet-based scheme for coding hyperspectral images that employs multiple regression analysis to exploit the relationships among spectral wavelet-transformed components. The scheme is based on a pyramidal prediction, using different regression models, to increase the statistical independence in the wavelet domain. For lossless coding, RWA has proven to be superior to other spectral transform like PCA and to the best and most recent coding standard in remote sensing, CCSDS-123.0. In this paper we show that RWA also allows progressive lossy-to-lossless (PLL) coding and that it attains a rate-distortion performance superior to those obtained with state-of-the-art schemes. To take into account the predictive significance of the spectral components, we propose a Prediction Weighting scheme for JPEG2000 that captures the contribution of each transformed component to the prediction process.

The coding and transmission of the massive datasets captured by Earth Observation (EO) satellites is a critical issue in current missions. The conventional approach is to use compression on board the satellite to reduce the size of the captured images. This strategy exploits spatial and/or spectral redundancy to achieve compression. Another type of redundancy found in such data is the temporal redundancy between images of the same area that are captured at different instants of time. This type of redundancy is commonly not exploited because the required data and computing power are not available on board the satellite. This paper introduces a coding scheme for EO satellites able to exploit this redundancy. Contrary to traditional approaches, the proposed scheme employs both the downlink and the uplink of the satellite. Its main insight is to compute and code the temporal redundancy on the ground and transmit it to the satellite via the uplink. The satellite then uses this information to compress more efficiently the captured image. Experimental results for Landsat 8 images indicate that the proposed dual link image coding scheme can achieve higher coding performance than traditional systems for both lossless and lossy regimes.

The Karhunen-Loeve Transform (KLT) is a popular transform used in multiple image processing scenarios. Sometimes, the application of the KLT is not carried out as a single transform over an entire image. Rather, the image is divided into smaller spatial regions (segments), each of which is transformed by a smaller dimensional KLT. Such a situation may penalize the transform efficiency. An improvement for the segmented KLT, aiming at mitigating discontinuities arising on the edge of adjacent regions, is proposed in this paper. In the case of moderately varying image regions, discontinuities occur as the consequence of disregarded similarity between transform domains, as the order and sign of eigenvectors in the transform matrices are mismatched. In the proposed method, the KLT is adjusted to guarantee the best achievable similarity via the optimal assignment and sign correspondence for eigenvectors. Experimental results indicate that the proposed transform improves the similarity between transform domains, and reduces RMSE on the edge of adjacent regions. In consequence, images processed by the adjusted KLT present better cohesion and continuity between independently transformed regions.

The recently proposed Regression Wavelet Analysis (RWA) scheme holds a great promise as a spectral transform for compressing hyperspectral images due to its low complexity, reversibility, and demonstrated superior coding performance. The scheme is based on a pyramidal prediction, using multiple regression analysis, to exploit statistical dependence in the wavelet domain. For lossless coding, RWA has proven to offer better performance than other spectral transforms like reversible PCA and also better than the best and most recent lossless coding standard in remote sensing, CCSDS-123.0. For progressive lossy-to-lossless coding, RWA also yields improved performance as compared to PCA. In this paper, we show that the RWA parameters are unbiased for lossy coding, where the regression models are used not with the original transformed components, but with the recovered ones, which lack some information due to the lossy reconstruction. As a byproduct, we also report that the Exogenous RWA model, a variant of RWA where the employed regression parameters have been trained using other images from the same sensor, still provides almost identical performance to that obtained by applying regression on the same image, thus showing that hyperspectral images with large sizes in the spectral dimension can be coded via RWA without side information and at a lower computational cost.

The analysis techniques applied to DNA microarray images are under active development. As new techniques become available, it will be useful to apply them to existing microarray images to obtain more accurate results. The compression of these images can be a useful tool to alleviate the costs associated to their storage and transmission. The recently proposed Relative Quantizer (RQ) coder provides the most competitive lossy compression ratios while introducing only acceptable changes in the images. However, images compressed with the RQ coder can only be reconstructed with a limited quality, determined before compression. In this work, a progressive lossy-to-lossless scheme is presented to solve this problem. First, the regular structure of the RQ intervals is exploited to define a lossy-to-lossless coding algorithm called the Progressive RQ (PRQ) coder. Second, an enhanced version that prioritizes a region of interest, called the PRQ-region of interest (ROI) coder, is described. Experiments indicate that the PRQ coder offers progressivity with lossless and lossy coding performance almost identical to the best techniques in the literature, none of which is progressive. In turn, the PRQ-ROI exhibits very similar lossless coding results with better rate-distortion performance than both the RQ and PRQ coders.

A novel wavelet-based scheme to increase coefficient independence in hyperspectral images is introduced for lossless coding. The proposed regression wavelet analysis (RWA) uses multivariate regression to exploit the relationships among wavelet-transformed components. It builds on our previous nonlinear schemes that estimate each coefficient from neighbor coefficients. Specifically, RWA performs a pyramidal estimation in the wavelet domain, thus reducing the statistical relations in the residuals and the energy of the representation compared to existing wavelet-based schemes. We propose three regression models to address the issues concerning estimation accuracy, component scalability, and computational complexity. Other suitable regression models could be devised for other goals. RWA is invertible, it allows a reversible integer implementation, and it does not expand the dynamic range. Experimental results over a wide range of sensors, such as AVIRIS, Hyperion, and Infrared Atmospheric Sounding Interferometer, suggest that RWA outperforms not only principal component analysis and wavelets but also the best and most recent coding standard in remote sensing, CCSDS-123.

DNA microarrays are one of the fastest-growing new technologies in the field of genetic research, and DNA microarray images continue to grow in number and size. Since analysis techniques are under active and ongoing development, storage, transmission and sharing of DNA microarray images need be addressed, with compression playing a significant role. However, existing lossless coding algorithms yield only limited compression performance (compression ratios below 2:1), whereas lossy coding methods may introduce unacceptable distortions in the analysis process. This work introduces a novel Relative Quantizer (RQ), which employs non-uniform quantization intervals designed for improved compression while bounding the impact on the DNA microarray analysis. This quantizer constrains the maximum relative error introduced into quantized imagery, devoting higher precision to pixels critical to the analysis process. For suitable parameter choices, the resulting variations in the DNA microarray analysis are less than half of those inherent to the experimental variability. Experimental results reveal that appropriate analysis can still be performed for average compression ratios exceeding 4.5:1.

Compression is a key component in many imaging systems in order to accommodate limited resources such as power and bandwidth. Image compression is often done independent of the specific tasks that the systems are designed for, such as target detection, classification, diagnosis, etc. Standard compression techniques are designed based on quality metrics such as mean-squared error (MSE) or peak signal to noise ratio (PSNR). Recently, a metric based on task-specific information (TSI) was proposed and successfully incorporated into JPEG2000 encoding. It has been shown that the proposed TSI metric can optimize the task performance. In this work, a joint metric is proposed to provide a seamless transition between the conventional quality metric MSE and the recently proposed TSI. We demonstrate the effectiveness and flexibility of the proposed joint TSI metric for target detection tasks. Furthermore, it is extended to video tracking applications to demonstrate the robustness of the proposed metric. Experimental results show that although the metric is not directly designed for the applied task, better tracking performance can still be achieved when the joint metric is used, compared to results obtained with the traditional MSE metric.

Spectral transforms are tools commonly employed in multi- and hyperspectral data compression to decorrelate images in the spectral domain. The pairwise orthogonal transform (POT) is one such transform that has been specifically devised for resource-constrained contexts similar to those found on board satellites or airborne sensors. Combining the POT with a 2-D coder yields an efficient compressor for multi- and hyperspectral data. However, a drawback of the original POT is that its dynamic range expansion, i.e., the increase in bit depth of transformed images, is not constant, which may cause problems with hardware implementations. Additionally, the dynamic range expansion is often too large to be compatible with the current 2-D standard CCSDS 122.0-B-1. This paper introduces the isorange POT, a derived transform that has a small and limited dynamic range expansion, compatible with CCSDS 122.0-B-1 in almost all scenarios. Experimental results suggest that the proposed transform achieves lossy coding performance close to that of the original transform. For lossless coding, the original POT and the proposed isorange POT achieve virtually the same performance.

A methodology for visually lossless compression of monochrome stereoscopic 3D images is proposed. Visibility thresholds are measured for quantization distortion in JPEG2000. These thresholds are found to be functions of not only spatial frequency, but also of wavelet coefficient variance, as well as the gray level in both the left and right images. To avoid a daunting number of measurements during subjective experiments, a model for visibility thresholds is developed. The left image and right image of a stereo pair are then compressed jointly using the visibility thresholds obtained from the proposed model to ensure that quantization errors in each image are imperceptible to both eyes. This methodology is then demonstrated via a particular 3D stereoscopic display system with an associated viewing condition. The resulting images are visually lossless when displayed individually as 2D images, and also when displayed in stereoscopic 3D mode.

In this paper, a compressive detection approach for detecting the presence of multiple Frequency Hopping Spread Spectrum (FHSS) signals is introduced. The large bandwidth produced due to hopping between frequency channels makes interception of FHSS signals challenging. Conventional FHSS detection approaches use channelized radiometers which reduce the sampling rate by dividing the entire FHSS hopping frequency range into sub bands. In this paper, we introduce a detection system which compressively observes the entire FHSS hopping range and can detect not only the presence of FHSS signals but also the number of signals present. Theoretical analysis and simulations are provided to compare the performance of the proposed compressive detection system to a channelized radiometer based detection system.

Compression is a key component in imaging systems hat have limited power, bandwidth, or other resources. In many applications, images are acquired to support a specific task such as target detection or classification. However, standard image compression techniques such as JPEG or JPEG2000 (J2K) are often designed to maximize image quality as measured by conventional quality metrics such as mean-squared error (MSE) or Peak Signal to Noise Ratio (PSNR). This mismatch between image quality metrics and ask performance motivates our investigation of image compression using a task-specific metric designed for the designated tasks. Given the selected target detection task, we first propose a metric based on conditional class entropy. The proposed metric is then incorporated into a J2K encoder to create compressed codestreams that are fully compliant with the J2K standard. Experimental results illustrate that the decompressed images obtained using the proposed encoder greatly improve performance in detection/classification tasks over images encoded using a conventional J2K encoder.

Visually lossless coding allows image codecs to achieve high compression ratios while producing images without visually noticeable distortion. In general, visually lossless coding is approached from the point of view of the encoder, so most methods are not applicable to already compressed codestreams. This paper presents two algorithms focused on the visually lossless decoding and transmission of JPEG2000 codestreams. The proposed strategies can be employed by a decoder, or a JPIP server, to reduce the decoding or transmission rate without penalizing the visual quality of the resulting images.

Parallel processing is key to augmenting the throughput of image codecs. Despite numerous efforts to parallelize wavelet-based image coding systems, most attempts fail at the parallelization of the bitplane coding engine, which is the most computationally intensive stage of the coding pipeline. The main reason for this failure is the causality with which current coding strategies are devised, which assumes that one coefficient is coded after another. This work analyzes the mechanisms employed in bitplane coding and proposes alternatives to enhance opportunities for parallelism. We describe a stationary probability model that, without sacrificing the advantages of current approaches, removes the main obstacle to the parallelization of most coding strategies. Experimental tests evaluate the coding performance achieved by the proposed method in the framework of JPEG2000 when coding different types of images. Results indicate that the stationary probability model achieves similar coding performance, with slight increments or decrements depending on the image type and the desired level of parallelism.

With consumer-grade 3D displays becoming widely available, stereoscopic 3D imaging has received increased attention over the last few years. Since a left eye and a right eye image are contained in a stereo pair, stereoscopic imaging requires double the amount of data compared to 2D images. Therefore, efficient data compression techniques are especially critical in these applications. In this paper, we propose a method to determine visibility thresholds (VTs) for 3D displays with active shutter glasses. These VTs are then used in a novel visually lossless compression method for monochrome stereoscopic 3D images.

DNA micro arrays are state-of-the-art tools in biological and medical research. In this work, we discuss the suitability of lossy compression for DNA micro array images and highlight the necessity for a distortion metric to assess the loss of relevant information. We also propose one possible metric that considers the basic image features employed by most DNA micro array analysis techniques. Experimental results indicate that the proposed metric can identify and differentiate important and unimportant changes in DNA micro array images.

Visually lossless coding is a method through which an image is coded with numerical losses that are not noticeable by visual inspection. Contrary to numerically lossless coding, visually lossless coding can achieve high compression ratios. In general, visually lossless coding is approached from the point of view of the encoder, i.e., as a procedure devised to generate a compressed code stream from an original image. If an image has already been encoded to a very high fidelity (higher than visually lossless - perhaps even numerically lossless), it is not straightforward to create a just visually lossless version without fully re-encoding the image. However, for large repositories, re-encoding may not be a suitable option. A visually lossless decoder might be useful to decode, or to parse and transmit, only the data needed for visually lossless reconstruction. This work introduces a decoder for JPEG 2000 code streams that identifies and decodes the minimum amount of information needed to produce a visually lossless image. The main insights behind the proposed method are to estimate the variance of the code blocks before the decoding procedure, and to determine the visibility thresholds employing a well-known model from the literature. The main advantages are faster decoding and the possibility to transmit visually lossless images employing minimal bit rates.

Summary form only given. Visually lossless image compression methods aim to compress images while ensuring that compression distortions are below perceptible levels. In medical imaging applications where high bit-depth images are often displayed on lower bit-depth displays, adjustments of image brightness and contrast during display are very common. In these applications, radiologists often change the display window level and width to view different ranges of intensities within the full range to better visualize diverse types of tissue in the image. However, when an image created to be visually lossless at a particular display setting is manipulated prior to display, compression distortions that were initially invisible may become visible. Similarly, compression artifacts that would be visible in certain window settings can be invisible in others, creating opportunities for the compression algorithm to allow increased compression distortion with corresponding increases in compression ratios. In this work, the effects of window level and window width adjustments on visibility thresholds were investigated. A JPEG2000 based image compression method to achieve visually lossless compression for a given window level and width was then proposed. A validation study was performed to confirm that the images obtained using the proposed method cannot be distinguished from original windowed images. The proposed compression method was also extended to a client-server setting where the server transmits incremental data to the client to ensure visually lossless representation after adjustments to the window level and width are made at the client side. The proposed incremental compression method was compared to a reference compression system where an 8-bit image corresponding to the desired window settings is created from a 12-bit CT image first at the encoder. This image is then compressed to achieve visually lossless compression using the methods described in. When the window settings are updated, a new 8-bit image corresponding to the updated window settings is created and compressed in a visually lossless manner. A comparison of the two methods illustrate that while the reference system is more efficient when the display settings are changed only once, the proposed method is advantageous when the display settings are changed more than once, requiring only 18% of the data transmitted by the reference system at the end of seven window setting adjustments.

In this letter, we propose a compressive sensing scheme for the mixture estimation problem in spectroscopy. We show that by applying an appropriate measurement matrix on the chemical mixture spectrum, we obtain an overall measurement matrix which is sparse. This enables the use of a low-complexity iterative reconstruction algorithm, called the interval-passing algorithm, to estimate the concentration of each chemical present in the mixture. Simulation results for the proportion of correct reconstructions show that chemical mixtures with a large number of chemicals present can be recovered.

Due to exponential growth in image sizes, visually lossless coding is increasingly being considered as an alternative to numerically lossless coding, which has limited compression ratios. This paper presents a method of encoding color images in a visually lossless manner using JPEG2000. In order to hide coding artifacts caused by quantization, visibility thresholds (VTs) are measured and used for quantization of subband signals in JPEG2000. The VTs are experimentally determined from statistically modeled quantization distortion, which is based on the distribution of wavelet coefficients and the dead-zone quantizer of JPEG2000. The resulting VTs are adjusted for locally changing backgrounds through a visual masking model, and then used to determine the minimum number of coding passes to be included in the final codestream for visually lossless quality under the desired viewing conditions. Codestreams produced by this scheme are fully JPEG2000 Part-I compliant.

Computed tomography (CT) is a noninvasive medical test obtained via a series of X-ray exposures resulting in 3-D images that aid medical diagnosis. Previous approaches for coding such 3-D images propose to employ multicomponent transforms to exploit correlation among CT slices, but these approaches do not always improve coding performance with respect to a simpler slice-by-slice coding approach. In this paper, we propose a novel analysis which accurately predicts when the use of a multicomponent transform is profitable. This analysis models the correlation coefficient r based on image acquisition parameters readily available at acquisition time. Extensive experimental results from multiple image sensors suggest that multicomponent transforms are appropriate for images with correlation coefficient r in excess of 0.87.

This work introduces a rate allocation method for the transmission of pre-encoded JPEG2000 video over time-varying channels, which vary their capacity during video transmission due to network congestion, hardware failures, or router saturation. Such variations occur often in networks and are commonly unpredictable in practice. The optimization problem is posed for such networks and a rate allocation method is formulated to handle such variations. The main insight of the proposed method is to extend the complexity scalability features of the FAst rate allocation through STeepest descent (FAST) algorithm. Extensive experimental results suggest that the proposed transmission scheme achieves near-optimal performance while expending few computational resources.

Preclinical imaging is a key enabling technology for medical research, such as in drug discovery, cancer detection, or osteoporosis screening. Europe is investing a significant amount of resources in a distributed phenotype study, carried out using Micro Computed Tomography (CT) images, which are acquired at very high resolutions to ease the detection of skeleton malformations. Unfortunately, like other CT images, MicroCT images commonly contain a notable amount of noise emitted by the acquisition device, which hinders the encoding of such images. In order to improve coding performance, we propose to use the Hounsfield Scale to establish a relationship between CT values and biological tissue together with an air-filtering approach, which modifies only samples located outside the biological area without penalizing the usefulness of the images to clinical experts, nor the visual perception of images. The proposed filter sets all samples having value lower than a specific threshold T to a constant figure.

DNA micro arrays are useful to identify the function and regulation of a large number of genes in a single experiment, even whole genomes. In this work, we analyze the relationship between DNA micro array image histograms and the compression performance of loss less JPEG2000. Also, a reversible transform based on histogram swapping is proposed. Intensive experimental results using different coding parameters are discussed. Results suggest that this transform improves previous loss less JPEG2000 results on all DNA micro array image sets.

Embedded quantization is a mechanism employed by lossy image coding systems to successively refine the distortion of an image. Commonly, it is conducted through a uniform scalar dead zone quantizer (USDQ) together with a bitplane coding strategy (BPC). Although this scheme is convenient for current hardware architectures and achieves competitive coding performance, it establishes the embedded quantizer without allowing major variations. This paper studies the design of non-restricted embedded quantizers with the aim to determine a quantization scheme that provides (near-)optimal performance for the lossy compression of images at low rates. Results suggest that optimally designed quantization schemes can achieve slightly better performance than that of USDQ+BPC by employing a non-uniform quantizer that requires fewer quantization stages.

A class of two-bit bit flipping algorithms for decoding low-density parity-check codes over the binary symmetric channel was proposed in [1]. Initial results showed that decoders which employ a group of these algorithms operating in parallel can offer low error floor decoding for high-speed applications. As the number of two-bit bit flipping algorithms is large, designing such a decoder is not a trivial task. In this paper, we describe a procedure to select collections of algorithms that work well together. This procedure relies on a recursive process which enumerates error configurations that are uncorrectable by a given algorithm. The error configurations uncorrectable by a given algorithm form its trapping set profile. Based on their trapping set profiles, algorithms are selected so that in parallel, they can correct a fixed number of errors with high probability.

Scanning orders of bitplane image coding engines are commonly envisaged from theoretical or experimental insights and assessed in practice in terms of coding performance. This paper evaluates classic scanning strategies of modern bitplane image codecs using several theoretical-practical mechanisms conceived from rate-distortion theory. The use of these mechanisms allows distinguishing those features of the bitplane coder that are essential from those that are not. This discernment can aid the design of new bitplane coding engines with some special purposes and/or requirements. To emphasize this point, a low-complexity scanning strategy is proposed. Experimental evidence illustrates, assesses, and validates the proposed mechanisms and scanning orders.

We present a method to construct low-density parity-check (LDPC) codes with low error floors on the binary symmetric channel. Codes are constructed so that their Tanner graphs are free of certain small trapping sets. These trapping sets are selected from the trapping set ontology for the Gallager A/B decoder. They are selected based on their relative harmfulness for a given decoding algorithm. We evaluate the relative harmfulness of different trapping sets for the sum–product algorithm by using the topological relations among them and by analyzing the decoding failures on one trapping set in the presence or absence of other trapping sets. We apply this method to construct structured LDPC codes. To facilitate the discussion, we give a new description of structured LDPC codes whose parity-check matrices are arrays of permutation matrices. This description uses Latin squares to define a set of permutation matrices that have disjoint support and to derive a simple necessary and sufficient condition for the Tanner graph of a code to be free of four cycles.

Rate allocation methods are used for variable bit rate (VBR) video transmission. The Fast rate allocation through STepest descent (FAST) is a rate allocation method that achieves valid solutions in scenarios where buffer and bandwidth may vary from client to client, respecting buffer limits and fulfilling real time processing requeriments in a JPEG2000 framework. This work restates the optimization problem and implements an expansion for FAST algorithm, allowing the adaptation to possible changes in channel bandwidth while video still transferring. The proposed method considers buffer's fullness, bandwidth changes and number of frames remainig to transfer in order to provide a valid solution. Experimental results suggest the validity of solutions whereas maintaining the initial version features.

This paper introduces an adaptive scanning order for bitplane image coding engines, which is devised from a rate-distortion optimization perspective that uses recent advances in coefficient modeling and distortion estimation. The main idea is to always select the next coefficient to be coded so that image distortion and code stream length are minimized. The sequence of visited coefficients is adapted as more coefficients are coded without need to explicitly transmit order information. Experimental results suggest that the proposed scanning order produces a code stream in which nearly all bits lie on the convex hull of the operation rate-distortion function.

Radiation exposure increases the risk of inducing cancer in a patient when Computed Tomography is performed. Radiologists reduce the radiation dose to minimize the risk of cancer, consequently, noise is introduced in the image, considerably penalizing its quality. To enhance the quality of the image, different noise filters are developed. The filtered noise can be discarded due to its insignificance for the medical diagnosis. In this research, a coding scheme for Computed Tomography with JPEG2000 including a noise filtering stage is proposed. The JPEG2000 standard is selected in our coding approach because: it is supported in DICOM, the communication protocol in the medical setting, it includes progressive lossy-to-lossless coding, and it incorporates JPIP, the interactive transmission protocol. Extensive experimental tests using different images suggest that noise filtering does not penalize the visual quality, but allows substantial improvements in coding performance.

JPEG2000 images with K wavelet decomposition levels inherently support K + 1 spatial resolutions. Other resolutions can be obtained through resampling adjacent native resolution images. In this paper, we propose a method of encoding images that can be decoded at arbitrary resolutions in a visually lossless manner with minimum bitrates. To achieve this, visibility thresholds for resampled quantization distortions are studied, and a technique of applying thresholds that vary with the display resolution is introduced.

In this paper, we propose a new class of bit flipping algorithms for low-density parity-check (LDPC) codes over the binary symmetric channel (BSC). Compared to the regular (parallel or serial) bit flipping algorithms, the proposed algorithms employ one additional bit at a variable node to represent its “strength.” The introduction of this additional bit increases the guaranteed error correction capability by a factor of at least 2. An additional bit can also be employed at a check node to capture information which is beneficial to decoding. A framework for failure analysis of the proposed algorithms is described. These algorithms outperform the Gallager A/B algorithm and the min-sum algorithm at much lower complexity. Concatenation of two-bit bit flipping algorithms show a potential to approach the performance of belief propagation (BP) decoding in the error floor region, also at lower complexity.

This work addresses the transmission of pre-encoded JPEG2000 video within a video-on-demand scenario. The primary requirement for the rate allocation algorithm deployed in the server is to match the real-time processing demands of the application. Scalability in terms of complexity must be provided to supply a valid solution by a given instant of time. The FAst rate allocation through STeepest descent (FAST) method introduced in this work selects an initial (and possibly poor) solution, and iteratively improves it until time is exhausted or the algorithm finishes execution. Experimental results suggest that FAST commonly achieves solutions close to the global optimum while employing very few computational resources.

The visibility threshold is the maximum contrast where a stimulus remains invisible. This value varies with the spatial frequency and orientation of the stimulus and is often used in determining the quantization step size of a subband for visually lossless coding. When an image is displayed at a different resolution level, the spatial frequency of each subband is also changed. Previous JPEG2000 visually lossless algorithms have a single set of quantization step sizes optimized for the full display resolution. This implies that if the image is rendered at reduced resolution there are significant amounts of extraneous information in the codestream. In this paper we present a new coding method which effectively incorporates multiple quantization step sizes into the JPEG2000 framework. This allows for visually lossless decoding at a variety of resolutions, using only a portion of the full resolution codestream.

This paper introduces a class of structured low-density parity-check (LDPC) codes whose parity check matrices are arrays of permutation matrices. The permutation matrices are obtained from Latin squares and form a finite field under some matrix operations. They are chosen so that the Tanner graphs do not contain subgraphs harmful to iterative decoding algorithms. The construction of column-weight-three codes is presented. Although the codes are optimized for the Gallager A/B algorithm over the binary symmetric channel (BSC), their error performance is very good on the additive white Gaussian noise channel (AWGNC) as well.

We propose a closed-form rate allocation scheme (RAS) for entropy-coded uniform scalar quantization of dependent sources in classification problems. The proposed RAS is applicable to nonbinary classification with piecewise monotonic unquantized Bayes decision boundaries. The RAS is also extended to joint compression and classification.

The relation between the girth and the guaranteed error correction capability of¿-left-regular low-density parity-check (LDPC) codes when decoded using the bit flipping (serial and parallel) algorithms is investigated. A lower bound on the size of variable node sets which expand by a factor of at least3 ¿/4is found based on the Moore bound. This bound, combined with the well known expander based arguments, leads to a lower bound on the guaranteed error correction capability. The decoding failures of the bit flipping algorithms are characterized using the notions of trapping sets and fixed sets. The relation between fixed sets and a class of graphs known as cage graphs is studied. Upper bounds on the guaranteed error correction capability are then established based on the order of cage graphs. The results are extended to left-regular and right-uniform generalized LDPC codes. It is shown that this class of generalized LDPC codes can correct a linear number of worst case errors (in the code length) under the parallel bit flipping algorithm when the underlying Tanner graph is a good expander. A lower bound on the size of variable node sets which have the required expansion is established.

The relation between the girth and the error correction capability of column-weight-three LDPC codes under the Gallager A algorithm is investigated. It is shown that a column-weight-three LDPC code with Tanner graph of girthg¿ 10 can correct all error patterns with up to(g/2-1) errors in at mostg/2 iterations of the Gallager A algorithm. For codes with Tanner graphs of girthg¿ 8, it is shown that girth alone cannot guarantee correction of all error patterns with up to(g/2-1) errors under the Gallager A algorithm. Sufficient conditions to correct(g/2-1) errors are then established by studying trapping sets.

Distortion estimation techniques are often employed in bitplane coding engines to minimize the computational load, or the memory requirements, of the encoder. A common approach is to determine distortion estimators that approximate the mean squared error decreases when data are successively coded and transmitted. Such estimators usually assume that coefficients are uniformly distributed in the quantization interval. Even though this assumption simplifies estimation, it does not exactly correspond with the nature of the signal. This work introduces new distortion estimators determined through a precise approximation of the coefficient's distribution within the quantization intervals. Experimental results obtained when our estimators are used for the post-compression rate-distortion optimization process of JPEG2000 suggest that they are able to approximate distortion with very high accuracy.

Video transmission is a topic of major interest in our community. One of the most fundamental aspects behind many video transmission schemes is the bit allocation problem that occurs when variable bitrate video is transmitted under multiple rate constraints. Rate constraints are imposed by the channel capacity, buffer sizes in the server and/or client, and by the coding system. This work introduces a rate allocation method conceived from hill climbing techniques with steepest descent that requires very few computational resources. Experimental results achieved within the framework of JPEG2000 suggest that our approach commonly achieves a solution close to the global optimum.

Remote visualization of volumetric images has gained importance over the past few years in medical and industrial applications. Volume visualization is a computationally intensive process, often requiring hardware acceleration to achieve a real time viewing experience. One remote visualization model that can accomplish this would transmit rendered images from a server, based on viewpoint requests from a client. For constrained server-client bandwidth, an efficient compression scheme is vital for transmitting high quality rendered images. In this paper, we present a new view compensation scheme that utilizes the geometric relationship between viewpoints to exploit the correlation between successive rendered images. The proposed method obviates motion estimation between rendered images, enabling significant reduction to the complexity of a compressor. Additionally, the view compensation scheme, in conjunction with JPEG2000 performs better than AVC, the state of the art video compression standard.

Bitplane coding is a common strategy used in current image coding systems to perform lossy, or lossy-to-lossless, compression. There exist several studies and applications employing bitplane coding that require estimators to approximate the distortion produced when data are successively coded and transmitted. Such estimators usually assume that coefficients are uniformly distributed in the quantization interval. Even though this assumption simplifies estimation, it does not exactly correspond with the nature of the signal. This work introduces new estimators to approximate the distortion produced by the successive coding of transform coefficients in bitplane image coders, which have been determined through a precise approximation of the coefficients' distribution within the quantization intervals. Experimental results obtained in three applications suggest that the proposed estimators are able to approximate distortion with very high accuracy, providing a significant improvement over state-of-the-art results.

In this paper, we address issues concerning bilevel image compression using JPEG2000. While JPEG2000 is designed to compress both bilevel and continuous tone image data using a single unified framework, there exist significant limitations with respect to its use in the lossless compression of bilevel imagery. In particular, substantial degradation in image quality at low resolutions severely limits the resolution scalable features of the JPEG2000 code-stream. We examine these effects and present two efficient methods to improve resolution scalability for bilevel imagery in JPEG2000. By analyzing the sequence of rounding operations performed in the JPEG2000 lossless compression pathway, we introduce a simple pixel assignment scheme that improves image quality for commonly occurring types of bilevel imagery. Additionally, we develop a more general strategy based on the JPIP protocol, which enables efficient interactive access of compressed bilevel imagery. It may be noted that both proposed methods are fully compliant with Part 1 of the JPEG2000 standard.

Summary form only given. JPEG2000 Part 2 by Taubman, D.S. and Marcellin, M.W. (2002) supports the use of multi-component transforms (MCT) to decorrelate multi-component image data along the component direction. Three types of point transforms are allowed: linear block transforms, dependency transforms and wavelet transforms. Besides these transforms, Part 2 also allows grouping of arbitrary subsets of components into 'component collections.' Any of the three transform types can be used for each such collection. These Part 2 extensions have been used for compressing 3-D images in many applications such as medical, hyper-spectral and video. It is widely believed that the MCT extensions are applicable only to 3-D data. In this work, we demonstrate their use for compressing N-datasets for any N ges 3. Let (M1, M2,..., MN-2, X, Y) denote the dimensions (size) of an N-dataset. The data can then be interpreted as a collection of 2-D (X, Y) images (or components). The number of such components is T = pik=1N-2Mk. Each component is indexed by an N - 2 dimensional vector: [m1,m2,...,mN-2] where mkisin {1, 2,..., Mk}. To compress an N-D dataset, the T components are processed by N - 2 stages of MCT. Specifically, each stage has T input components and produces T output components, which in turn are used as inputs to the next stage. Consider the kth(k isin {1, 2,..., N - 2}) stage of the MCT which is used to decorrelate the data along the k dimension. The input components are first reordered to form collections. A collection is formed by grouping all components having the same value of the N - 3 dimensional index [m1, m2,...,mk+1,...mN-2]. There are then T/Mkcollections, each containing Mkcomponents. For each collection, a point transform is applied, resulting in decorrelation of the data along the kthdimension. The output components of the final MCT stage (N - 2) are compressed using the 2-D wavelet transform and block coding of JPEG2000. It should be noted that the encoding procedure described above will result in a code-stream that is entirely compliant with JPEG2000 Part 2. Two major applications of multi-dimensional sources are medical images (fMRI, 4-D cardiac and ultrasound) and multi-view video. We have applied the proposed methodology to a 5-D (X, Y, slice, time, trial) fMRI dataset. A PSNR (Peak Signal to Noise Ratio) gain of 8 to 14.9 dB is observed at various bit rates, when 3 stages of MCT (5-D) are used as opposed to the usual single stage of MCT (3-D). The proposed scheme can support scalability in all N dimensions. Additionally, incremental processing can be used to achieve considerable savings in memory usage.

Remote visualization of volumetric images has gained importance over the past few years in medical and industrial applications. Volume visualization is a computationally intensive process, often requiring hardware acceleration to achieve a real time viewing experience. One remote visualization model that can accomplish this would transmit rendered images (with dedicated hardware) from the server based on view-point requests from a client. For a given server-client bandwidth, an efficient compression scheme is vital for transmitting high quality rendered images. In this paper, we present a new compression scheme that utilizes the geometric relationship between view-points to exploit the correlation between successive rendered images. The proposed method performs better than AVC, the state of the art video compression standard. Additionally, our scheme obviates motion estimation between rendered images, enabling significant reduction to the complexity of the encoder.

We investigate the relation between the girth and the guaranteed error correction capability of gamma-left regular LDPC codes when decoded using the bit flipping (serial and parallel) algorithms. A lower bound on the number of variable nodes which expand by a factor of at least 3gamma/4 is found based on the Moore bound. An upper bound on the guaranteed correction capability is established by studying the sizes of smallest possible trapping sets.

JPEG2000 Part 2 supports the use of multicomponent transforms (MCTs) to decorrelate multicomponent images along the component direction. Such point transforms can be performed on arbitrary subsets of components, known as "component collections." These Part 2 extensions have been used for compressing 3-D images in applications such as medical imaging and remote sensing. It is widely believed that the MCT extensions are applicable only to 3-D data. In this letter, we demonstrate their use for compressing N-D datasets for any N ges 3.

In this paper, we address issues regarding bi-level image compression using JPEG2000. While JPEG2000 is designed to compress both bi-level and continuous tone imagery using a single unified framework, there exist significant limitations with respect to its use in the lossless compression of bi-level imagery. In particular, substantial degradation in image quality at low resolutions severely limits the resolution scalable features of the JPEG2000 code-stream. We analyze these effects and present two efficient methods to improve resolution scalability for bi-level imagery in JPEG2000. It may be noted that both proposed methods are compliant with Part-I of the JPEG2000 standard

A novel rate-optimal rate allocation algorithm is proposed for parallel transmission of scalable images in multi-channel systems. Scalable images are transmitted via fixed-length packets. The proposed algorithm selects a subchannel as well as a channel code rate for each packet, based on the signal-to-noise ratios (SNR) of the subchannels. The resulting scheme provides unequal error protection of source bits. Applications to JPEG2000 transmission show that significant UEP gains are achieved over equal error protection (EEP) schemes.

An important issue in wireless sensor networks is the limited energy and bandwidth resources. A novel theory is developed here for maximizing the lifetime of unicast multihop wireless sensor networks. An optimal centralized solution is presented in the form of an iterative algorithm. The algorithm attempts to find a Pareto-optimal (PO) solution. In the first iteration, the minimum lifetime of the network is maximized. If the solution is not PO, a second iteration is performed which maximizes the second minimum lifetime, subject to the minimum lifetime being maximum. At the nth iteration, the algorithm maximizes the nth minimum lifetime subject to the (n-1) the minimum lifetime being maximum, subject to the (n-2) the minimum lifetime being maximum, etc. The algorithm can be stopped at any iteration n. The presented solution assumes static network conditions

In this letter, we investigate the error resilience properties of JPEG2000. We identify the dependencies among coding passes of a codeblock codestream, and determine the sections of the codestream that can be salvaged in the presence of errors. In our analysis, we consider the effects of mode variations provided by the standard. The proposed methods are derived using the existing dependency structure of coding passes and do not require a substantial increase in computational complexity of the decoder. Experimental results indicate that the proposed methods can improve the error resilience performance substantially.

A fast rate-optimal rate allocation algorithm is proposed for parallel transmission of scalable images in multichannel systems. Scalable images are transmitted via fixed-length packets. The proposed algorithm selects a subchannel, as well as a channel code rate for each packet, based on the signal-to-noise ratios (SNRs) of the subchannels. The resulting scheme provides unequal error protection of source bits and significant gains are obtained over equal error protection schemes. An application of the proposed algorithm to JPEG2000 transmission shows the advantages of exploiting differences in SNRs between subchannels. Multiplexing of multiple sources is also considered, and additional gains are achieved by exploiting information diversity among the sources.

A framework is proposed for iterative joint source-channel decoding of JPEG2000 codestreams. At the encoder, JPEG2000 is used to perform source coding with certain error-resilience (ER) modes, and LDPC codes are used to perform channel coding. During decoding, the source decoder uses the ER modes to identify corrupt sections of the codestream and provides this information to the channel decoder. Decoding is carried out jointly in an iterative fashion. Experimental results indicate that the proposed method requires fewer iterations and improves overall system performance.

JPEG2000 is the latest international standard for image compression. The rich feature set and the state of the art image compression performance make JPEG2000 an attractive alternative for many applications. One of the latest applications to utilize JPEG2000 is digital cinema. Digital Cinema Initiatives (DCI) is a joint venture of seven major Hollywood studios. DCI was formed to establish specifications for an open architecture for digital cinema. As part of these specifications, DCI has selected JPEG2000 for future distribution of motion pictures. In this paper, we provide a brief overview of the DCI specification and discuss how JPEG2000 is utilized within this specification

Distributed imaging using sensor arrays is gaining popularity among various research and development communities. A common bottleneck within such an imaging sensor network is the large resulting data load. In applications for which transmission power and/or bandwidth are constrained, this can drastically decrease the sensor network lifetime. We present an algorithm that efficiently exploits inter- and intrasensor correlation for the purpose of power-constrained distributed transmission of sensor-network imagery. Gains in network lifetime up to 114% are obtained when using the suggested algorithm with lossless compression. Our results also demonstrate that when lossy compression is employed, much larger gains are achieved. For example, when a normalized root-mean-squared error of 0.78% can be tolerated in the received measurements, the network lifetime increases by a factor of 2.8, as compared to the (optimized) lossless case.

One of the goals of telemedicine is to enable remote visualization and browsing of medical volumes. There is a need to employ scalable compression schemes and efficient client-server models to obtain interactivity and an enhanced viewing experience. First, we present a scheme that uses JPEG2000 and JPIP (JPEG2000 Interactive Protocol) to transmit data in a multi-resolution and progressive fashion. The server exploits the spatial locality offered by the wavelet transform and packet indexing information to transmit, in so far as possible, compressed volume data relevant to the clients query. Once the client identifies its volume of interest (VOI), the volume is refined progressively within the VOI from an initial lossy to a final lossless representation. Contextual background information can also be made available having quality fading away from the VOI. Second, we present a prioritization that enables the client to progressively visualize scene content from a compressed file. In our specific example, the client is able to make requests to progressively receive data corresponding to any tissue type. The server is now capable of reordering the same compressed data file on the fly to serve data packets prioritized as per the client's request. Lastly, we describe the effect of compression parameters on compression ratio, decoding times and interactivity. We also present suggestions for optimizing JPEG2000 for remote volume visualization and volume browsing applications. The resulting system is ideally suited for client-server applications with the server maintaining the compressed volume data, to be browsed by a client with a low bandwidth constraint.

The widespread adoption of the JPEG2000 standard calls for the development of computationally efficient algorithms to analyze the content of imagery compressed using this standard. For this purpose, we propose the use of the information content (IC) of wavelet subbands, defined as the number of bytes that JPEG2000 spends to encode the subbands. The IC of subbands can be obtained from the packet headers of the JPEG2000 codestream, thereby avoiding decompressing the arithmetically encoded bitplane data. We present experimental results for two content analysis tasks; namely, image classification and scene change detection. Our results indicate that performance comparable to that of methods operating on decompressed data can be achieved, while saving computational and bandwidth resources.

Summary form only given. In this work, we extend the methods of (Z. Wu et al, Proc. IEEE Global Telecom. Conf. (Globecom), 2004) to video coding. The proposed algorithm considers the transmission of one or more motion JPEG2000 (MJP2) coded video sequence(s) over a constant bit rate channel. Rate distortion diversity over several dimensions is exploited. This work demonstrates that by minimizing the expected overall distortion at the receiver, both improved average quality of the frames (quality multiplexing gain), as well as more uniform quality across the frames (variance multiplexing gain) can be achieved. Experimental results illustrate the efficiency of the proposed methods.

In this paper, we present methods that can be used to conceal errors in corrupt motion JPEG2000 codestreams. Motion JPEG2000 is an intra-frame compression technique and the proposed concealment methods utilize motion compensation to conceal errors. The simulation results indicate that the proposed methods can yield over 10 dB improvement in PSNR.

There is increased use of medical imaging techniques that produce four dimensional (4D) datasets such as fMRI and 3D dynamic echocardiograms. These datasets consume even larger amounts of resources for transmission or storage compared to the traditional 2D data sets. In this paper, we extend the zero tree algorithms, EZW (embedded zero tree coding of wavelet coefficients) and SPIHT (set partitioning in hierarchical trees) to 4D to compress the 4D datasets more efficiently. Integer to integer wavelet transforms scaled by appropriate subband energy weights are used to get lossy to lossless compression. We also investigate the effects of lossy compression on the end result of fMRI analysis.

This paper presents an unequal error protection scheme based on the Plotkin construction for channel (error control) codes. The resulting codes offer the novel ability of using one long channel codeword to protect an entire image, yet still allowing progressive decoding. Progressive quality improvements occur in two ways: the first is the usual progressive refinement, where image quality is improved as more data are received; the second is that residual error rates of earlier received data are reduced as more data are received.

A joint source/channel coding algorithm is proposed for the transmission of multiple image sources over memoryless channels. The proposed algorithm uses a quality scalable image coder to optimally allocate a limited bit budget among all the sources to achieve the optimal overall distortion reduction for the multiple reconstructed images. In addition to the conventional un gain, it provides channel multiplexing gain, which can be much more significant. Furthermore, an extended scheme is proposed to provide flexibility between the optimization performance and complexity.

The high compression efficiency and various features provided by JPEG2000 make it attractive for image transmission purposes. A novel joint source/channel coding scheme tailored for JPEG2000 is proposed in this paper to minimize the end-to-end image distortion within a given total transmission rate through memoryless channels. It provides unequal error protection by combining the forward error correction capability from channel codes and the error detection/localization functionality from JPEG2000 in an effective way. The proposed scheme generates quality scalable and error-resilient codestreams. It gives competitive performance with other existing schemes for JPEG2000 in the matched channel condition case and provides more graceful quality degradation for mismatched cases. Furthermore, both fixed-length source packets and fixed-length channel packets can be efficiently formed with the same algorithm.

This paper describes the wavelet compression of ECG signals by JPEG2000. JPEG2000 is the latest international standard for compression of still images. JPEG2000 codec is designed to compress images and it can also be used to compress other signals. The desirable characteristics of the JPEG2000 codec, such as precise rate control and progressive quality, are retained in the presented ECG compression scheme. To compress the ECG data using a JPEG2000 code, the one-dimensional ECG sequence is processed to produce a two-dimensional matrix. This matrix is then encoded using JPEG2000.

A joint source/channel coding algorithm is proposed for the transmission of multiple image sources over memoryless channels. The proposed algorithm uses a quality scalable image coder to allocate a limited bit budget optimally among all the sources to achieve the optimal overall distortion reduction for the multiple reconstructed images. In addition to the conventional unequal error protection gain, it provides channel multiplexing gain which can be much more significant. Furthermore, an extended scheme is proposed to provide flexibility between the optimization performance and complexity.

In this paper, a robust image transmission scheme based on JPEG2000 is proposed for packet erasure channels. Error resilience functionalities provided by JPEG2000 are utilized to control the source coding efficiency and the robustness according to channel conditions. Furthermore, together with the proposed interleaving scheme, some erasures can be recovered. Experimental results show the effectiveness of the scheme.

The error resilience properties of JPEG2000 are investigated. Through a careful analysis of the structure of the JPEG2000 codestreams, the dependencies among coding passes of a codeblock are identified. In the analysis, the effects of mode variations provided by the standard for low-memory, low-complexity implementations are considered. The proposed methods are derived using the existing dependency structure of coding passes and do not require a substantial increase in computational capabilities of the decoder. Experimental results indicate that these methods can improve error resilience performance substantially.

A two-level, hybrid-optimization scheme is proposed for rate allocation of JPEG2000 (J2K) transmission over noisy channels. It combines forward error correction (FEC) and the J2K error resilience modes to minimize the expected end-to-end image distortion. Subject to a total target bit rate, it forms fixed-length channel packets in a straightforward manner. The proposed scheme utilizes the source distortion rate characteristics and the BER statistics of the available channel codes. It has complexity less than 0 (R/sup 2/) for a specific J2K codestream, where R is the number of available channel code rates. Performance results with both RCPC and turbo codes for fixed-length source packets are provided to show the effectiveness of the scheme.

With the increasing importance of heterogeneous networks and time-varying communication channels, fine scalability has become a highly desirable feature in both image and video coders. A single highly scalable bitstream can provide precise rate control for constant bitrate (CBR) traffic and accurate quality control for variable bitrate (VBR) traffic. We first propose two leaky-bucket rate allocation methods that provide constant quality video under buffer constraints. These methods can be used with all scalable coders. Moreover, we make use of one of these methods (DBRC) and extend it so that it can be used when multiple sequences are multiplexed over a single communications channel. The goal is to allocate the capacity of the channel between sequences to achieve constant quality across all sequences. Experimental results using Motion JPEG2000 demonstrate substantial benefits.

A method for joint source/channel rate allocation for transmission of JPEG2000 (J2K) codestreams over noisy channels is proposed. The rate allocation method uses the BER statistics of different channel codes and the rate-distortion characteristics of the source generated by the J2K encoder. The features of the J2K codestream are utilized to form a multilayer codestream and an unequal amount of protection is applied to each layer. The proposed method is capable of accommodating different channel coding strategies. Experimental results indicate that the presented method compares favorably with methods existing in the literature.

JPEG2000 is the latest image compression standard to emerge from the Joint Photographic Experts Group (JPEG) working under the auspices of the International Standards Organization. Although the new standard does offer superior compression performance to JPEG, JPEG2000 provides a whole new way of interacting with compressed imagery in a scalable and interoperable fashion. This paper provides a tutorial-style review of the new standard, explaining the technology on which it is based and drawing comparisons with JPEG and other compression standards. The paper also describes new work, exploiting the capabilities of JPEG2000 in client-server systems for efficient interactive browsing of images over the Internet.

We attempt to quantify the coding gain due to parent-child dependencies in subband transforms. These dependencies are generally credited for the excellent mean square error (MSE) performances of zero-tree-based compression algorithms such as embedded zero-tree coding of wavelet coefficients (EZW) and set partitioning in hierarchical trees (SPIHT). Our experiments indicate that this gain is significantly smaller than what is widely believed at the time of this writing.

Blur identification is a crucial first step in many image restoration techniques. An approach for identifying image blur using vector quantizer encoder distortion is proposed. The blur in an image is identified by choosing from a finite set of candidate blur functions. The method requires a set of training images produced by each of the blur candidates. Each of these sets is used to train a vector quantizer codebook. Given an image degraded by unknown blur, it is first encoded with each of these codebooks. The blur in the image is then estimated by choosing from among the candidates, the one corresponding to the codebook that provides the lowest encoder distortion. Simulations are performed at various bit rates and with different levels of noise. Results show that the method performs well even at a signal-to-noise ratio (SNR) as low as 10 dB.

JPEG-2000 is an emerging standard for still image compression. This paper provides a brief history of the JPEG-2000 standardization process, an overview of the standard, and some description of the capabilities provided by the standard. Part I of the JPEG-2000 standard specifies the minimum compliant decoder, while Part II describes optional, value-added extensions. Although the standard specifies only the decoder and bitstream syntax, in this paper we describe JPEG-2000 from the point of view of encoding. We take this approach, as we believe it is more amenable to a compact description more easily understood by most readers.

JPEG 2000 will soon be an international standard for still image compression. This paper describes that standard at a high level, indicates the component pieces which empower the standard, and gives example applications which highlight differences between JPEG 2000 and prior image compression standards.

Reversible integer wavelet transforms allow both lossless and lossy decoding using a single bitstream. We present a new fully scalable image coder and investigate the lossless and lossy performance of these transforms in the proposed coder. The lossless compression performance of the presented method is comparable to JPEG-LS. The lossy performance is quite competitive with other efficient lossy compression methods.

This correspondence presents an improved version of an algorithm designed to perform image restoration via nonlinear interpolative vector quantization (NLIVQ). The improvement results from using lapped blocks during the decoding process. The algorithm is trained on original and diffraction-limited image pairs. The discrete cosine transform is again used in the codebook design process to control complexity. Simulation results are presented which demonstrate improvements over the nonlapped algorithm in both observed image quality and peak signal-to-noise ratio. In addition, the nonlinearity of the algorithm is shown to produce super-resolution in the restored images.

We study the problem of memory-efficient scalable image compression and investigate some tradeoffs in the complexity versus coding efficiency space. The focus is on a low-complexity algorithm centered around the use of sub-bit-planes, scan-causal modeling, and a simplified arithmetic coder. This algorithm approaches the lowest possible memory usage for scalable wavelet-based image compression and demonstrates that the generation of a scalable bit-stream is not incompatible with a low-memory architecture.

Synthetic aperture radar (SAR) is a remote-sensing technology that uses the motion of the radar transmitter to synthesize an antenna aperture much larger than the actual antenna aperture to yield high-spatial resolution radar images. In this paper, trellis-coded quantization (TCQ) techniques are shown to provide a high-performance, low bit-error sensitivity solution to the problem of downlink data rate reduction for SAR systems. Trellis-coded vector quantization (TCVQ) and universal TCQ coding systems are discussed, implemented, and compared with other data compression schemes [block adaptive quantization (BAQ) and VQ] that can be used to compress SAR phase history data.

In this work, we present coding techniques that enable progressive transmission when trellis coded quantization (TCQ) is applied to wavelet coefficients. A method for approximately inverting TCQ in the absence of least significant bits is developed. Results are presented using different rate allocation strategies and different entropy coders. The proposed wavelet-TCQ coder yields excellent coding efficiency while supporting progressive modes analogous to those available in JPEG.

A new form of trellis coded quantization based on uniform quantization thresholds and "on-the-fly" quantizer training is presented. The universal trellis coded quantization (UTCQ) technique requires neither stored codebooks nor a computationally intense codebook design algorithm. Its performance is comparable with that of fully optimized entropy-constrained trellis coded quantization (ECTCQ) for most encoding rates. The codebook and trellis geometry of UTCQ are symmetric with respect to the trellis superset. This allows sources with a symmetric probability density to be encoded with a single variable-rate code. Rate allocation and quantizer modeling procedures are given for UTCQ which allow access to continuous quantization rates. An image coding application based on adaptive wavelet coefficient subblock classification, arithmetic coding, and UTCQ is presented. The excellent performance of this coder demonstrates the efficacy of UTCQ. We also present a simple scheme to improve the perceptual performance of UTCQ for certain imagery at low bit rates. This scheme has the added advantage of being applied during image decoding, without the need to reencode the original image.

Complex phase history data in synthetic aperture radar (SAR) systems require extensive processing before useful images can be obtained. In spotlight mode SAR systems, useful images can be obtained by applying aperture weighting and inverse Fourier transform operations to SAR phase history data. In this paper, we are concerned with the compression of the complex phase history data obtained by a spotlight SAR system. We exploit knowledge of the aperture weighting function along with Fourier transform processing to attach a "gain" factor to each complex phase history data sample. This gain factor is then used to efficiently allocate bits to the phase history data during quantization. Performance evaluations are presented for this compression system relative to other existing SAR phase history data compression systems.

A novel lossless medical image compression algorithm based on three-dimensional integer wavelet transforms and zerotree coding is presented. The EZW algorithm is extended to three dimensions and context-based adaptive arithmetic coding is used to improve its performance. The algorithm (3-D CB-EZW) efficiently encodes image volumes by exploiting the dependencies in all three dimensions, while enabling lossy and lossless compression from the same bitstream. Results on lossless compression of CT and MR images are presented, and compared to other lossless compression algorithms. The progressive performance of the 3-D CB-EZW algorithm is also compared to other lossy progressive coding algorithms. For representative images, the 3-D CB-EZW algorithm produced an average of 14% and 20% decrease in compressed file sizes for CT and MR images, respectively, compared to the best available 2-D lossless compression techniques.

We present a wavelet based non-linear interpolative vector quantization scheme for joint compression and restoration of images; two tasks which are traditionally regarded as having conflicting goals. Vector quantizer codebook training is done using a training set consisting of pairs of the original image and its diffraction-limited counterpart. The designed VQ is then used to compress and simultaneously restore diffraction-limited images. Results from simulations indicate that the image produced at the output of the decoder is quantitatively and visually superior to the diffraction-limited image at the input to the encoder. We also compare the performance of several wavelet filters in our algorithm.

Algorithms for image recovery with super-resolution from sequences of short-exposure images are presented. Both deconvolution from wavefront sensing (DWFS) and blind deconvolution are explored. A multiframe algorithm is presented for DWFS which is based on maximum a posteriori (MAP) formulation. A multiframe blind deconvolution algorithm is presented based on a maximum likelihood formulation with strict constraints incorporated using nonlinear reparameterizations. Quantitative simulation of imaging through atmospheric turbulence and wavefront sensing are used to demonstrate the super-resolution performance of the algorithms.

This paper proposes a new error concealment algorithm for packet video, effectively eliminating error propagation effects. Most standard video codecs use motion compensation to remove temporal redundancy. With such motion-compensated interframe processing, any packet loss may generate serious error propagation over more than ten consecutive frames. This kind of error propagation leads to perceptually annoying artifacts. Thus, proper error concealment algorithms need to be used to reduce this effect. The proposed algorithm adopts a one-pixel block overlap structure. With the redundancy information from the damaged frame and the following frames, the proposed POCS-based method can have consistently high performance in error concealment. According to the experimental results, the proposed algorithm can successfully eliminate visible error propagation. In addition, the proposed algorithm is very robust. Experimental results show that it can have good error concealment results, even when the damaged frame loses all the DCT coefficients.

A near-lossless image compression scheme is presented. It is essentially a differential pulse code modulation (DPCM) system with a mechanism incorporated to minimize the entropy of the quantized prediction error sequence. With a "near-lossless" criterion of no more than a d gray-level error for each pixel, where d is a small nonnegative integer, trellises describing all allowable quantized prediction error sequences are constructed. A set of "contexts" is defined for the conditioning prediction error model and an algorithm that produces minimum entropy conditioned on the contexts is presented. Finally, experimental results are given.

This paper presents a novel technique for image restoration based on nonlinear interpolative vector quantization (NLIVQ). The algorithm performs nonlinear restoration of diffraction-limited images concurrently with quantization. It is trained on image pairs consisting of an original image and its diffraction-limited counterpart. The discrete cosine transform is used in the codebook design process to control complexity. Simulation results are presented that demonstrate improvements in visual quality and peak signal-to-noise ratio of the restored images.

A fast and efficient quantization technique is described. It is fixed-length, robust to bit errors, and compatible with most current compression standards. It is based on entropy-constrained quantization and uses the well-known and efficient Viterbi algorithm to force the coded sequence to be fixed-rate. Run-length coding techniques are used to improve the performance at low encoding rates. Simulation results show that it can achieve performance comparable to that of Huffman coded entropy-constrained scalar quantization with computational complexity increasing only linearly in block length.

Many systems in widespread use concentrate on the imaging of binary objects, e.g., the archival storage of text documents on microfilm or the facsimile transmission of text. Due to the imperfect nature of such systems, the binary image is unavoidably corrupted by blur and noise to form a grey-scale image. We present a technique to reverse this degradation which maps the binary object reconstruction problem into a Viterbi state-trellis. We assign states of the trellis to possible outcomes of the reconstruction estimate and search the trellis in the usual optimal fashion. Our method yields superior estimates of the original binary object over a wide range of signal-to-noise ratios (SNR) when compared with conventional Wiener filter (WF) estimates. For moderate blur and SNR levels, the estimates produced approach the maximum likelihood (ML) bound on estimation performance.

Synthetic aperture radar (SAR) is a remote-sensing technology which uses the motion of the radar transmitter to synthesize an antenna aperture much larger than the actual antenna aperture to yield high spatial resolution radar images. Trellis coded quantization (TCQ) techniques are shown to provide a high performance, low bit-error sensitivity solution to the problem of downlink data rate reduction for SAR systems. Trellis coded vector quantization (TCVQ) and universal trellis coded quantization (UTCQ) coding systems are implemented and compared with other data compression schemes (block adaptive quantization (BAQ or BFPQ) and vector quantization (VQ)) that can be used to compress SAR phase history data.

As image coders evolve from DCT-based to wavelet-based designs, the latter must be enhanced to include capabilities currently supported by standards such as JPEG. Said and Pearlman (see IEEE Transactions on Circuits and Systems for Video Tech., vol.6, p.243-50, 1996) and Schwartz, Zandi and Boliek (see Proc. of SPIE, vol.2564, July 1995) have described approaches for incorporating progressive transmission capabilities within wavelet-based coders. All of these coders apply scalar quantization to wavelet transform coefficients and then apply sophisticated entropy coding methods to the quantized coefficients. In this paper, we present coding techniques that enable progressive transmission when trellis coded quantization (TCQ) is applied to the wavelet coefficients. While the trellis coded quantizer is more complex than the uniform scalar quantizer, comparable PSNR performance is achieved with a simple entropy coder. In addition, our use of sophisticated quantization and bit rate allocation algorithms enables the development of coders that are tuned for improved perceptual image quality.

A training-sequence-based entropy-constrained predictive trellis coded quantization (ECPTCQ) scheme is presented for encoding autoregressive sources. For encoding a first-order Gauss-Markov source, the mean squared error (MSE) performance of an eight-state ECPTCQ system exceeds that of entropy-constrained differential pulse code modulation (ECDPCM) by up to 1.0 dB. In addition, a hyperspectral image compression system is developed, which utilizes ECPTCQ. A hyperspectral image sequence compressed at 0.125 b/pixel/band retains an average peak signal-to-noise ratio (PSNR) of greater than 43 dB over the spectral bands.

This paper investigates various classification techniques, applied to subband coding of images, as a way of exploiting the nonstationary nature of image subbands. The advantages of subband classification are characterized in a rate-distortion framework in terms of "classification gain" and overall "subband classification gain." Two algorithms, maximum classification gain and equal mean-normalized standard deviation classification, which allow unequal number of blocks in each class, are presented. The dependence between the classification maps from different subbands is exploited either directly while encoding the classification maps or indirectly by constraining the classification maps. The trade-off between the classification gain and the amount of side information is explored. Coding results for a subband image coder based on classification are presented. The simulation results demonstrate the value of classification in subband coding.

We present an algorithm which combines vector quantization (VQ) techniques with variable size block matching algorithms (BMA) for motion compensated image sequence coding. First, an algorithm which decomposes the image frame into blocks of different sizes with similar motion parameters is proposed. Since the algorithm is based on splitting the block that would result in the highest decrease in distortion, it does not require a merging step. Next, VQ techniques are utilized to expedite the search for the block motion vector without significant sacrifice in the temporal entropy reduction performance. We compare the performance and the computational requirements of the proposed algorithm with other BMAs.

An algorithm based on nonlinear interpolative vector quantization (NLIVQ) is presented which accomplishes image restoration concurrently with image compression. The algorithm is applied to the problem of deblurring noise-free diffraction-limited images by training with a large set of blurred and original image pairs. Simulation results demonstrate a quantitative improvement in images processed by the algorithm, as measured by image peak signal-to-noise ratio (PSNR), as well as a significant improvement in perceived image quality. A theoretical formulation of the algorithm is presented along with a discussion of implementation, training and simulation results.

This paper proposes a new algorithm for error concealment in the presence of packet loss for block based video codecs. It uses a one-pixel overlap block structure. With out any smoothness assumption on block boundaries, this method provides consistently high performance in lost block recovery. The one-pixel overlap generally decreases the compression ratio for a fixed quantization step size. However, increases in PSNR obtained by averaging overlapping pixels largely compensates the effect. Experimental results show that there is almost no performance decrease in PSNR vs. compression ratio for dthe "fdctootball" and "Miss America" videos when using 9/spl times/9 blocks with one-pixel overlap (in place of the usual 8/spl times/8 blocks with no overlap).

A near-lossless image compression scheme is presented. It is essentially a DPCM system with a mechanism incorporated to minimize the entropy of the quantized prediction error sequence. With a "near-lossless" criterion of no more than a d gray level error for each pixel, where d is a small non-negative integer, trellises describing all allowable quantized prediction error sequences are constructed. A set of "contexts" is defined for the conditioning prediction error model and an algorithm that produces minimum entropy conditioned on the contexts is presented. Finally, experimental results are given.

Two systems are presented for compression of hyperspectral imagery which utilize trellis coded quantization (TCQ). Specifically, the first system uses TCQ to encode transform coefficients resulting from the application of an 8/spl times/8/spl times/8 discrete cosine transform (DCT). The second systems uses DPCM to spectrally decorrelate the data, while a 2D DCT coding scheme is used for spatial decorrelation. Side information and rate allocation strategies are discussed. Entropy-constrained code-books are designed using a modified version of the generalized Lloyd algorithm. These entropy constrained systems achieve compression ratios of greater than 70:1 with average PSNRs of the coded hyperspectral sequences exceeding 40.0 dB.>

A predictive image coder having minimal decoder complexity is presented. The image coder utilizes recursive interpolative DPCM in conjunction with adaptive classification, entropy-constrained trellis coded quantization, and optimal rate allocation to obtain signal-to-noise ratios (SNRs) in the range of those provided by the most advanced transform coders.

The discrete wavelet transform has recently emerged as a powerful technique for decomposing images into various multi-resolution approximations. Multi-resolution decomposition schemes have proven to be very effective for high-quality, low bit-rate image coding. In this work, we investigate the use of entropy-constrained trellis-coded quantization (ECTCQ) for encoding the wavelet coefficients of both monochrome and color images. ECTCQ is known as an effective scheme for quantizing memoryless sources with low to moderate complexity, The ECTCQ approach to data compression has led to some of the most effective source codes found to date for memoryless sources. Performance comparisons are made using the classical quadrature mirror filter bank of Johnston and nine-tap spline filters that were built from biorthogonal wavelet bases. We conclude that the encoded images obtained from the system employing nine-tap spline filters are marginally superior although at the expense of additional computational burden. Excellent peak-signal-to-noise ratios are obtained for encoding monochrome and color versions of the 512x512 "Lenna" image. Comparisons with other results from the literature reveal that the proposed wavelet coder is quite competitive.

Modulation coding, to limit the number of consecutive zeros in a data stream, is essential in digital magnetic recording/playback systems. Additionally, such systems require error-correction coding to ensure that the decoded output matches the recorder input, even if noise is present. Typically, these two coding steps have been performed independently, although various methods of combining them into one step have recently appeared. Another recent development is two-dimensional modulation codes, which meet runlength constraints using several parallel recording tracks, significantly increasing channel capacity. The article combines these two ideas. Previous techniques (both block and trellis structures) for combining error correction and modulation coding are surveyed, with discussion of their applicability in the two-dimensional case. One approach, based on trellis-coded modulation, is explored in detail, and a class of codes is developed which exploits the increased capacity to achieve good error-correcting ability at the same rate as common non-error-correcting one-dimensional codes.>

A new construction for n-track (d, k) codes with redundancy r, referred to as (d, k; n, r) codes, is presented. This construction applies single-track (d, k+/spl Delta/k) codes (with certain extra constraints and appropriate amounts of delay) on each of the n tracks. This construction achieves a large part of the capacity increases possible when using (d, k; n, r) codes, has simple encoders and decoders, and exhibits considerable robustness to faulty tracks. It is shown that under this construction, (d, k; n, r) codes can achieve at least (n-r-1:)/n*100% of the gap in capacity between conventional (d, k) and (d, /spl infin/) codes. Several practical examples of (d, k; n, r) codes under this construction are presented.>

A training-sequence-based entropy-constrained predictive trellis coded quantization (ECPTCQ) scheme is presented for encoding autoregressive sources. For encoding a first-order Gauss-Markov source, the MSE performance of an 8-state ECPTCQ system exceeds that of entropy-constrained DPCM by up to 1.0 dB. In addition, a hyperspectral image compression system is developed which utilizes ECPTCQ. A hyperspectral image sequence compressed at 0.15 bits/pixel/band retains peak signal-to-noise ratios greater than 42 dB over most spectral bands.>

A predictive image coder having minimal decoder complexity is presented. The image coder utilizes recursive interpolative DPCM in conjunction with adaptive classification, entropy-constrained trellis coded quantization, and optimal rate allocation to obtain signal-to-noise ratios in the range of the most advanced transform coders.>

In this work, we propose end-to-end image compression/decompression systems based on the discrete wavelet transform (DWT), entropy-constrained trellis coded quantization (ECTCQ), and arithmetic coding. Two forms of adaptation (codebook and image subblock) are investigated. The baseline system uses neither form of adaptation, while the adaptive systems use one or both forms of adaptivity. Excellent peak signal-to-noise ratios are obtained for the 512/spl times/512 "Lenna" image. Comparison of the systems with other results from the literature show that the fully adaptive system is quite competitive.>

An entropy-constrained trellis coded quantization (TCQ) scheme is presented for encoding memoryless sources. A simple 8-state trellis is used to encode the memoryless Gaussian source with mean-squared-error (MSE) performance within about 0.5 dB of the rate-distortion function. This performance is achieved at all non-negative encoding rates.>

Investigates the performance of different adaptive prediction algorithms for use in a low delay, high-quality speech coder at 18 kbit/s. The system to be studied is based on trellis coded quantization (TCQ). In the work presented, the authors demonstrate that when trellises based on feedback-free convolutional encoding circuits are employed, error propagation in TCQ systems is not a serious problem. Performance comparisons are made using a fixed predictor and four different adaptive predictors. Informal listening tests reveal that the encoded speech is of good communications quality at an error rate of 10/sup -3/. In an ideal channel, a system employing an all-pole least squares lattice predictor with pitch prediction produces reconstructed speech that is nearly indistinguishable from the original.>

The application of transform coding using trellis coded quantization (TCQ) to encode monochrome and color images is investigated. Specifically, TCQ is used to encode transform coefficients resulting from applying a 16 * 16 discrete cosine transform (DCT) to 8-b gray level and 24-b color images. For the color images, the red, green, and blue (RGB) planes are transformed into the NTSC transmission primaries (Y,I,and Q) before the DCT is applied. Rate allocation schemes for encoding the transform coefficients and side information are presented and discussed. The generalized Lloyd algorithm and the entropy-constrained vector quantizer design algorithm are used to design fixed-rate and entropy-constrained codebooks. The entropy-constrained designs result in a low-complexity system with objective performance rivaling that of many coding schemes from the literature.>

Multitrack run-length-limited (d, k) modulation codes were recently introduced as a method to increase storage densities in magnetic and optical recording systems. These codes are a generalization of the usual run-length-limited (d, k) codes and provide for increased storage density by relaxing the k-constraint and encoding multiple tracks in parallel. This paper introduces a method to construct multitrack (d, k) block codes which are implemented via an enumeration scheme based on the trellis description of the (d, k) constraints. This implementation results in memory requirements which increase only linearly with block length as opposed to the exponential increases arising from look-up table implementations of block codes.>

Multitrack run-length-limited (d, k) modulation codes were recently introduced as a method to increase storage densities in magnetic and optical recording systems. These codes are a generalization of the usual run-length-limited (d, K) codes and provide for increased storage density by relaxing the k-constraint and encoding multiple tracks in parallel. Previously, n-track (d, K) codes used all n tracks to satisfy the K constraint causing synchronization to be completely intolerant of faulty tracks. The authors present multitrack (d, k) codes which allow for r faulty tracks by mandating that all subsets of n-r tracks satisfy the K constraint.>

A new class of run-length-limited codes in introduced. These codes are called two-dimensional or multitrack modulation codes. Two-dimensional modulation codes provide substantial data storage density increases for multitrack recording systems by operating on multiple tracks in parallel. Procedures for computing the capacity of these new codes are given along with fast algorithms for implementing these procedures. Examples of two-dimensional codes are given to provide a comparison between the encoding rates obtainable with multitrack and traditional single-track codes.>

Trellis-coded quantization is generalized to allow a vector reproduction alphabet. Three encoding structures are described, several encoder design rules are presented, and two design algorithms are developed. It is shown that for a stationary ergodic vector source, if the optimized trellis-coded vector quantization reproduction process is jointly stationary and ergodic with the source, then the quantization noise is zero-mean and of a variance equal to the difference between the source variance and the variance of the reproduction sequence. Several examples illustrate the encoder design procedure and performance.>

Trellis-coded quantization (TCQ) is incorporated into a transform coding structure for encoding monochrome images. Both fixed-rate and entropy-constrained designs are considered. The fixed-rate TCQ-based systems provide gains in peak signal-to-noise ratio of up to 4.13 dB over scalar quantizer-based schemes, while the entropy-constrained TCQ-based systems provide gains of up to 6.39 dB. The subjective quality of the TCQ-based systems is substantially better than that of comparable scalar quantizer-based systems. The high-frequency background noise, fuzziness, and block artifacts produced by the scalar quantizer-based systems are virtually eliminated by the TCQ-based systems.>

Trellis-coded quantization (TCQ) is developed and applied to the encoding of memoryless and Gauss-Markov sources. The theoretical justification for the approach is alphabet-constrained rate distortion theory, which is a dual to the channel capacity argument that motivates trellis-coded modulation (TCM). The authors adopt the notions of signal set expansion, set partitioning, and branch labeling of TCM, but modify the techniques to account for the source distribution, to design TCQ coders of low complexity with excellent mean-squared-error (MSE) performance. For a memoryless uniform source, TCQ provides an MSE within 0.21 dB of the distortion-rate bound at all positive (integral) rates. The performance is superior to that promised by the coefficient of quantization for all of the best lattices known in dimensions 24 or less. For a memoryless Gaussian source, the TCQ performance at rates of 0.5, 1, and 2 b/sample is superior to all previous results the authors found in the literature. The encoding complexity of TCQ is very modest. TCQ is incorporated into a predictive coding structure for the encoding of Gauss-Markov sources. Simulation results for first-, second-, and third-order Gauss-Markov sources are presented.>

An implementation of trellis coded quantization (TCQ) of memoryless sources on dedicated digital signal processing hardware is described. The effect of encoding delay (i.e. the TCQ symbol release rule) on the overall encoding performance is evaluated. It is shown that quite modest encoding delays cause negligible loss in performance, compared to very long (e.g. 1000 sample) trellis search depths. The hardware requirements of TCQ encoding are discussed. A TCQ implementation on the DSP32 and TMS32020 digital signal processing (DSP) integrated circuits is described. Analytical expressions are provided for the per source sample implementation complexity (in clock cycles) for each DSP integrated circuit.>

Two algorithms are presented that approximate complex numbers by elements of the algebraic integers of Q(w) where w=e/sup 2 pi i/8/. These algorithms substantially reduce the computational burden. Range and memory requirements are given as a function of the desired accuracy, and expressions are obtained for the number of computations required by each algorithm.>

Trellis coded quantization is incorporated into a predictive coding structure for encoding sampled speech. Systems are developed using fixed prediction/fixed residual encoding, fixed prediction/adaptive residual encoding, and adaptive prediction/adaptive residual encoding. For a fully adaptive 16 kbps speech coding system, segmental signal-to-noise ratios in the range of 17.5 to 20.2 dB are obtained for a variety of speakers and test sentences. Reconstructed speech obtained from this system can be described as being of excellent communications quality.>