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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.

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.

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.

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.

Super resolution (SR) reconstruction is often considered to be an inverse problem in the sense that unknown high resolution images are sought for giving low resolution images. Recent studies have shown that the sparsity regularisation used in compressed sensing (CS) reconstruction improves the performance of SR reconstruction. Furthermore, under the assumption that mutually similar regions exist within a natural image, non‐local (NL) estimation produces accurate estimates for given degraded images. The incorporation of this NL estimation in SR reconstruction has been shown to yield better reconstructions. In this study, the authors propose the use of block matching and three‐dimensional filtering with sharpening estimation as the regularisation constraint under the CS‐based SR framework. This estimation collects similar blocks and adaptively filters them by the shrinkage of the transform coefficients. It recovers detailed structures while attenuating ringing artefacts. In addition, a sharpening technique used in the estimation also emphasises edges. As a result, the proposed SR algorithm searches for the solution that is similar to this enhanced estimate from among all feasible solutions. The experimental results demonstrate that the proposed method provides high‐quality SR images, both numerically and subjectively.

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.

Summary form only given. Typical greedy algorithms for sparse reconstruction problems, such as orthogonal matching pursuit and iterative thresholding, seek strictly sparse solutions. Recent work in the literature suggests that given a priori knowledge of the distribution of the sparse signal coefficients, better results can be obtained by a weighted averaging of several sparse solutions. Such a combination of solutions, while not strictly sparse, approximates an MMSE estimator and can outperform strictly sparse solvers in terms of l-2 reconstruction error. We introduce a novel method for obtaining such an approximate MMSE estimator by replacing the deterministic thresholding operator of Iterative Hard Thresholding with a randomized version. We demonstrate the improvement in performance experimentally for both synthetic 1D signals and real images.

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.

Predictive coding has proven to be an effective method for lossless image compression. In predictive coding, untransmitted pixels are predicted based on the pixels already available at the decoder. Prediction errors are then compressed by entropy coding, and the original image can be reconstructed exactly at the decoder. More accurate prediction decreases the entropy of the prediction error, allowing for increased compression. Conventional image prediction methods rely on information from the immediate local neighborhood of each pixel. We introduce a novel predictor that leverages non-local structural similarities which have been shown to be effective in image denoising and deblurring applications. Experimental results show that the proposed method achieves state-of-the-art compression performance.

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.

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.

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.

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.

While initial compressed sensing (CS) recovery techniques operated under the implicit assumption that the sparse domain coefficients are independently distributed, recent results have indicated that integrating a statistical or structural dependence model of sparse domain coefficients into CS enhances recovery. In this paper, we present a method for exploiting empirical dependences among wavelet coefficients during CS recovery using a Bayes least-square Gaussian-scale-mixture model. The proposed model is successfully incorporated into several recent CS algorithms, including reweighted l(1) minimization (RL1), iteratively reweighted least squares, and iterative hard thresholding. Extensive experiments including comparisons with a state-of-the-art model-based CS method demonstrate that the proposed algorithms are highly effective at reducing reconstruction error and/or the number of measurements required for a desired reconstruction quality.

Dynamic magnetic resonance imaging (MRI) is commonly used to observe dynamic physiological changes in tissue or to study organs with mobile structures such as the heart. In order to accurately capture spatiotemporal changes, it is desirable to have dynamic images with high temporal resolution in addition to high spatial resolution. Due to the nature of data acquisition in current MRI systems, there exists a trade-off between temporal and spatial resolution. In this work, we present two methods for improving the spatiotemporal resolution in dynamic MRI using compressive sampling (CS). Experimental results illustrate that the proposed Bayes least squares-Gaussian scale mixtures (BLS-GSM) model-based CS algorithm compares favorably with other state-of-the-art compressive dynamic MRI techniques.

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.

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.

This paper presents a method for improving wavelet-based Compressed Sensing (CS) reconstruction algorithms by exploiting the dependencies among wavelet coefficients. During CS recovery, a simple measure of significance for each wavelet coefficient is calculated using a weighted sum of the (estimated) magnitudes of the wavelet coefficient, its highly correlated neighbors, and parent. This simple measure is incorporated into three CS recovery algorithms, Reweighted L1 minimization algorithms (RL1), Iteratively Reweighted Least Squares (IRLS), and Iterative Hard Thresholding (IHT). Experimental results using one-dimensional signals and images illustrate that the proposed method (i) improves reconstruction quality for a given number of measurements, (ii) requires fewer measurements for a desired reconstruction quality, and (iii) significantly reduces reconstruction time.

Compressed Sensing (CS) theory has gained attention recently as an alternative to the current paradigm of sampling followed by compression. Early CS recovery techniques operated under the implicit assumption that the transform coefficients in the sparsity domain are independently distributed. Recent works, however, demonstrated that exploiting the statistical dependencies between transform coefficients can further improve the recovery performance of CS. In this paper, we propose the use of a Gaussian Scale Mixtures (GSM) model in CS. This model can efficiently exploit the statistical dependencies between wavelet coefficients during CS recovery. The proposed model is incorporated into several recent CS techniques including Reweighted l1minimization (RL1), Iteratively Reweighted Least Squares (IRLS), and Iterative Hard Thresholding (IHT). Experimental results show that the proposed method improves reconstruction quality for a given number of measurements or requires fewer measurements for a desired reconstruction quality.

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.

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.

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.

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.

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

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.

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.

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.

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.

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 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.

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.

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.

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.

Presents findings on image processing applications to the continuous and automatic monitoring and verification of the status of control devices and vehicle speed estimation on the Interstate-15 Reversible High Occupancy Vehicle (HOV) lanes as examples of "IVHS at Work". The overall goal of the study has been to supply additional enhanced monitoring capabilities for the HOV operations. These capabilities have been intended to assist, rather than to eliminate the human operators from the loop. The authors describe this unique undertaking together with the issues related to the systems architecture, hardware and software components, the integration, image processing tools, and preliminary field test results.