A key aspect of image coding systems is the probability model employed to code the data. The more precise the probability estimates inferred by the model, the higher the coding efficiency achieved. In general, probability models adjust the estimates after coding every new symbol. The main difficulty to apply such a strategy to a highly parallel coding engine is that many symbols are coded simultaneously, so the probability adaptation requires a different approach. The strategy employed in previous works utilizes stationary estimates collected a priori from a training set. Its main drawback is that statistics are dependent of the image type, so different images require different training sets. This work introduces two probability models for a highly parallel architecture that, similarly to conventional systems, adapt probabilities while coding data. One of the proposed models estimates probabilities through a finite state machine, while the other employs the statistics of already coded symbols via a sliding window. Experimental results indicate that the latter approach improves the performance achieved by the other models, including that of JPEG2000 and High Throughput JPEG2000, at medium and high rates with only a slight increase in computational complexity.

Fast image codecs are a current need in applications that deal with large amounts of images. Graphics Processing Units (GPUs) are suitable processors to speed up most kinds of algorithms, especially when they allow fine-grain parallelism. Bitplane Coding with Parallel Coefficient processing (BPC-PaCo) is a recently proposed algorithm for the core stage of wavelet-based image codecs tailored for the highly parallel architectures of GPUs. This algorithm provides complexity scalability to allow faster execution at the expense of coding efficiency. Its main drawback is that the speedup and loss in image quality is controlled only roughly, resulting in visible distortion at low and medium rates. This paper addresses this issue by integrating techniques of visually lossless coding into BPC-PaCo. The resulting method minimizes the visual distortion introduced in the compressed file, obtaining higher-quality images to a human observer. Experimental results also indicate 12% speedups with respect to BPC-PaCo.

Predictive coding techniques are attractive for image codecs because they can yield high compression efficiency while spending few computational resources. In remote sensing, predictive techniques are employed in prominent standards to transmit images captured by Earth Observation (EO) satellites. Although EO satellites have full duplex capacity, compression standards for spatial data are devised to use the downlink only. Recently, we presented a dual-link image coding system that employs both the uplink and the downlink to accelerate the transmission of such images. The proposed system was introduced in the wavelet-based JPEG2000 standard, which is not well-suited for satellites due to its complexity. This letter approaches the dual-link scheme to a more suitable standard for spatial data based on predictive coding, more precisely, the Lossless Multispectral and Hyperspectral image compression standard CCSDS-123.0-B.2. The proposed method adapts the dual-link image coding scheme to CCSDS-123.0-B-2 by incorporating a quantizer, a lightweight arithmetic coder, and a rate control technique. Experimental results suggest that the resulting system achieves higher coding ratios than CCSDS-123.0-B-2 and JPEG2000 with dual link.

The advent of new technologies such as high dynamic range or 8K screens has enhanced the quality of digital images but it has also increased the codecs’ computational demands to process such data. This paper presents a video codec that, while providing the same coding features and performance as those of JPEG2000, can process 16K video in real time using a consumer-grade GPU. This high throughput is achieved with a technique that introduces complexity scalability to a bitplane coding engine, which is the most computationally complex stage of the coding pipeline. The resulting codec can trade throughput for coding performance depending on the user’s needs. Experimental results suggest that our method can double the throughput achieved by CPU implementations of the recently approved High-Throughput JPEG2000 and by hardwired implementations of HEVC in a GPU.

Very fast image and video codecs are a pursued goal both in the academia and the industry. This paper presents a complexity scalable and parallel bitplane coding engine for wavelet-based image codecs. The proposed method processes the coefficients in parallel, suiting hardware architectures based on vector instructions. Our previous work is extended with a mechanism that provides complexity scalability to the system. Such a feature allows the coder to regulate the throughput achieved at the expense of slightly penalizing compression efficiency. Experimental results suggests that, when using the fastest speed, the method almost doubles the throughput of our previous engine while penalizing compression efficiency by about 10%.

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.

The growth in the use of satellite images has generated the need for their fast compression, processing, and distribution. JPEG2000 is a widespread standard for the compression and transmission of such images once they are in the ground. Despite its advanced features and excellent coding performance, JPEG2000 demands significant computational resources. This paper introduces a wavelet-based codec that uses the JPEG2000 framework, but replaces its most computationally demanding coding stage by a highly parallel engine. When executed in Graphics Processing Units to code high-resolution satellite images, the proposed codec achieves speed-ups of up to 8× when compared to the fastest implementation of JPEG2000 executed in a multi-core platform.

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.

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.

The fast compression of images is a requisite in many applications like TV production, teleconferencing, or digital cinema. Many of the algorithms employed in current image compression standards are inherently sequential. High performance implementations of such algorithms often require specialized hardware like field integrated gate arrays. Graphics Processing Units (GPUs) do not commonly achieve high performance on these algorithms because they do not exhibit fine-grain parallelism. Our previous work introduced a new core algorithm for wavelet-based image coding systems. It is tailored for massive parallel architectures. It is called bitplane coding with parallel coefficient processing (BPC-PaCo). This paper introduces the first high performance, GPUbased implementation of BPC-PaCo. A detailed analysis of the algorithm aids its implementation in the GPU. The main insights behind the proposed codec are an efficient thread-to-data mapping, a smart memory management, and the use of efficient cooperation mechanisms to enable inter-thread communication. Experimental results indicate that the proposed implementation matches the requirements for high resolution (4 K) digital cinema in real time, yielding speedups of 30× with respect to the fastest implementations of current compression standards. Also, a power consumption evaluation shows that our implementation consumes 40× less energy for equivalent performance than state-of-the-art methods.

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.

Whole-slide images (WSIs) are high-resolution, 2D, color digital images that are becoming valuable tools for pathologists in clinical, research and formative scenarios. However, their massive size is hindering their widespread adoption. Even though lossy compression can effectively reduce compressed file sizes without affecting subsequent diagnoses, no lossy coding scheme tailored for WSIs has been described in the literature. In this paper, a novel strategy called OptimizeMCT is proposed to increase the lossy coding performance for this type of images. In particular, an optimization method is designed to find image-specific multi-component transforms (MCTs) that exploit the high inter-component correlation present in WSIs. Experimental evidence indicates that the transforms yielded by OptimizeMCT consistently attain better coding performance than the Karhunen-Loève Transform (KLT) for all tested lymphatic, pancreatic and renal WSIs. More specifically, images reconstructed at the same bitrate exhibit average PSNR values 2.85~dB higher for OptimizeMCT than for the KLT, with differences of up to 5.17 dB.

The lossless compression of Whole Slide pathology Images (WSIs) using HEVC is investigated in this paper. Recently proposed intra-prediction algorithms based on differential pulse-code modulation (DPCM) and edge prediction provide significant bitrate improvements for a wide range of natural and screen content sequences, including WSIs. However, coding times remain relatively high due to the high number (35) of modes to be tested. In this paper, FastIn-tra, a novel method that requires testing only four modes is proposed. Among these four modes, FastIntra introduces a novel median edge predictor designed to accurately predict edges in different directionalities. Performance evaluations on various WSIs show average compression time reductions of 23.5% with important lossless coding improvements as compared to current block-wise intra-prediction and DPCM-based methods.

Lossy compression techniques based on multi-component transformation (MCT) can effectively enhance the storage and transmission of whole-slide images (WSIs) without adversely affecting subsequent diagnosis processes. Component transforms that are designed for other types of images or that do not take into account all aspects of the compression algorithm applied on the transformed components yield suboptimal coding performance. Recently, an MCT optimization framework adapted to the particularities of the input WSI and the following compression was proposed, yielding superior coding performance than the state of the art. However, its time complexity is too high for practical purposes. In this work FastOptimizeMCT, a fast version of this framework based on smart sampling of regions depicting tissue, is proposed. Exhaustive experimental evidence indicates that FastOptimizeMCT exhibits reasonable time complexity results-similar to that of scanning the WSIs- and coding performance that outperforms the KLT and the OST by 1.47 dB and 1.07 dB, respectively.

Image coding systems have been traditionally tailored for multiple instruction, multiple data (MIMD) computing. In general, they partition the (transformed) image in codeblocks that can be coded in the cores of MIMD-based processors. Each core executes a sequential flow of instructions to process the coefficients in the codeblock, independently and asynchronously from the others cores. Bitplane coding is a common strategy to code such data. Most of its mechanisms require sequential processing of the coefficients. The last years have seen the upraising of processing accelerators with enhanced computational performance and power efficiency whose architecture is mainly based on the single instruction, multiple data (SIMD) principle. SIMD computing refers to the execution of the same instruction to multiple data in a lockstep synchronous way. Unfortunately, current bitplane coding strategies cannot fully profit from such processors due to inherently sequential coding task. This paper presents bitplane image coding with parallel coefficient (BPC-PaCo) processing, a coding method that can process many coefficients within a codeblock in parallel and synchronously. To this end, the scanning order, the context formation, the probability model, and the arithmetic coder of the coding engine have been re-formulated. The experimental results suggest that the penalization in coding performance of BPC-PaCo with respect to the traditional strategies is almost negligible.

The lossless intra-prediction coding modality of the High Efficiency Video Coding standard provides high coding performance while allowing frame-by-frame basis access to the coded data. This is of interest in many professional applications, such as medical imaging, automotive vision, and digital preservation in libraries and archives. Various improvements to lossless intra-prediction coding have been proposed recently, most of them based on sample-wise prediction using differential pulse code modulation (DPCM). Other recent proposals aim at further reducing the energy of intra-predicted residual blocks. However, the energy reduction achieved is frequently minimal due to the difficulty of correctly predicting the sign and magnitude of residual values. In this paper, we pursue a novel approach to this energy-reduction problem using piecewise mapping (pwm) functions. In particular, we analyze the range of values in residual blocks and apply accordingly a pwm function to map specific residual values to unique lower values. We encode the appropriate parameters associated with the pwm functions at the encoder, so that the corresponding inverse pwm functions at the decoder can map values back to the same residual values. These residual values are then used to reconstruct the original signal. This mapping is, therefore, reversible and introduces no losses. We evaluate the pwm functions on 4 × 4 residual blocks computed after DPCM-based prediction for lossless coding of a variety of camera-captured and screen content sequences. Evaluation results show that the pwm functions can attain the maximum bitrate reductions of 5.54% and 28.33% for screen content material compared with DPCM-based and block-wise intra-prediction, respectively. Compared with intra-block copy, piecewise mapping can attain the maximum bit-rate reductions of 11.48% for a camera-captured material.

Recent years have seen the upraising of a new type of processors strongly relying on the Single Instruction, Multiple Data (SIMD) architectural principle. The main idea behind SIMD computing is to apply a flow of instructions to multiple pieces of data in parallel and synchronously. This permits the execution of thousands of operations in parallel, achieving higher computational performance than with traditional Multiple Instruction, Multiple Data (MIMD) architectures. The level of parallelism required in SIMD computing can only be achieved in image coding systems via microscopic parallel strategies that code multiple coefficients in parallel. Until now, the only way to achieve microscopic parallelism in bit plane coding engines was by executing multiple coding passes in parallel. Such a strategy does not suit well SIMD computing because each thread executes different instructions. This paper introduces the first bit plane coding engine devised for the fine grain of parallelism required in SIMD computing. Its main insight is to allow parallel coefficient processing in a coding pass. Experimental tests show coding performance results similar to those of JPEG2000.

The main difficulty to implement modern image coding systems in a GPU is that the algorithms employed in the core of the coding scheme are inherently sequential. We recently proposed bitplane image coding with parallel coefficient processing (BPC-PaCo), a coding scheme that, contrarily to most systems, permits the processing of multiple coefficients of the image in parallel. This enables the use of SIMD computing, ideal for its implementation in a GPU. This paper introduces and evaluates the GPU implementation of BPC-PaCo employing two different strategies that tradeoff computational throughput and compression efficiency. The proposed implementation is compared to the best CPU and GPU implementations of JPEG2000, the state-of-the-art image compression standard. Experimental results indicate that BPC-PaCo achieves a computational throughput that is an order of magnitude superior to that achieved with such implementations with a small reduction in coding efficiency.

This paper proposes a rate control algorithm for lossless region of interest (RoI) coding in HEVC intra-coding. The algorithm is developed for digital pathology images and allows for random access to the data. Based on an input RoI mask, the algorithm first encodes the RoI losslessly. According to the bit rate spent on the RoI, it then encodes the background by using rate control in order to meet an overall target bit rate. In order to increase rate control accuracy, the algorithm uses an R-λ model to approximate the slope of the rate-distortion curve, and updates any related model parameters during the encoding process. Random access is attained by coding the data using independent tiles. Experimental results show that the proposed algorithm attains the overall bit rate very accurately while providing lossless reconstruction of the RoI.

Remote sensing images often need to be coded and/or transmitted with constrained computational resources. Among other features, such images commonly have high spatial, spectral, and bit-depth resolution, which may render difficult their handling. This letter introduces an embedded quantization scheme based on two-step scalar deadzone quantization (2SDQ) that enhances the quality of transmitted images when coded with a constrained number of bits. The proposed scheme is devised for use in JPEG2000. It is named cell-based 2SDQ since it uses cells, i.e., small sets of wavelet coefficients within the codeblocks defined by JPEG2000. Cells permit a finer discrimination of coefficients in which to apply the proposed quantizer. Experimental results indicate that the proposed scheme is especially beneficial for high bit-depth hyperspectral images.

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.

Entropy is a measure of a message uncertainty. Among others aspects, it serves to determine the minimum coding rate that practical systems may attain. This paper defines an entropy-based measure to evaluate context models employed in wavelet-based image coding. The proposed measure is defined considering the mechanisms utilized by modern coding systems. It establishes the maximum performance achievable with each context model. This helps to determine the adequateness of the model under different coding conditions and serves to predict with high precision the coding rate achieved by practical systems. Experimental results evaluate four well-known context models using different types of images, coding rates, and transform strategies. They reveal that, under specific coding conditions, some widely-spread context models may not be as adequate as it is generally thought. The hints provided by this analysis may help to design simpler and more efficient wavelet-based image codecs.

Context-adaptive binary arithmetic coding is a widespread technique in the field of image and video coding. Most state-of-the-art arithmetic coders produce a (long) codeword of a priori unknown length. Its generation requires a renormalization procedure to permit progressive processing. This paper introduces two arithmetic coders that produce multiple codewords of fixed length. Contrary to the traditional approach, the generation of fixed-length codewords does not require renormalization since the whole interval arithmetic is stored in the coder's internal registers. The proposed coders employ a new context-adaptive mechanism based on variable-size sliding window that estimates with high precision the probability of the symbols coded. Their integration in coding systems is straightforward as demonstrated within the framework of JPEG2000. Experimental tests indicate that the proposed coders are computationally simpler than the MQ coder of JPEG2000 and the M coder of HEVC while achieving superior coding efficiency.

The release of the CUDA Kepler architecture in March 2012 has provided Nvidia GPUs with a larger register memory space and instructions for the communication of registers among threads. This facilitates a new programming strategy that utilizes registers for data sharing and reusing in detriment of the shared memory. Such a programming strategy can significantly improve the performance of applications that reuse data heavily. This paper presents a register-based implementation of the Discrete Wavelet Transform (DWT), the prevailing data decorrelation technique in the field of image coding. Experimental results indicate that the proposed method is, at least, four times faster than the best GPU implementation of the DWT found in the literature. Furthermore, theoretical analysis coincide with experimental tests in proving that the execution times achieved by the proposed implementation are close to the GPU's performance limits.

Wavelet-based coding systems commonly employ uniform scalar deadzone quantization (USDQ) together with a bitplane coding strategy to progressively refine image quality. Our previous work presents a quantization scheme that employs 2 step sizes depending on the magnitude of the coefficients. This 2-step scalar deadzone quantization (2SDQ) scheme is introduced in the framework of JPEG2000 by modifying all coefficients within a codeblock to enhance the quality of the image while transmitting fewer bitplanes than those needed with a conventional USDQ scheme. This paper extends our prior work by applying the 2SDQ in a subblock level, i.e., in small sets of coefficients, called cells, selected within a codeblock. Combined with rate-distortion optimization techniques, the proposed cell-based 2SDQ can help to code high quality images employing even fewer bitplanes than those needed with our previous strategy. This may be especially useful for high-dynamic range images or for devices with constrained resources.

This work defines an entropy-based measure aimed to establish the compression limits of wavelet-based image coding. This measure serves to appraise the efficiency of current codecs, determining whether there is margin for their improvement or not. Also, it may help to design new compression schemes that target a particular type of images and/or compression rates.

This works focuses on the High Efficiency Video Coding (HEVC) standard as a compression method to be potentially adopted by the Digital Imaging and Communications in Medicine (DICOM) standard. We are particularly interested in improving the lossless compression efficiency of the intra coding process for grayscale anatomical medical images. We focus on intra coding due to its low complexity and outstanding compression results, as well as the fact that it allows coding high-dimensional medical images on a slice-by-slice basis. This is especially advantageous for cases when only a small set of slices needs to be accessed without the need to decode the entire data set. Based on the characteristics of grayscale anatomical medical images, specifically their large amount of edge information and frequent number of patterns depicted on various directions, we propose improvements to HEVC intra coding based on sample-by-sample (SbS) differential pulse code modulation (DPCM) with equal displacements so the density of prediction modes is constant in all directions. Performance evaluations over MRI, CT and X-ray angiography sequences show that the proposed improvements outperform current HEVC lossless intra coding, achieving average coding gains of 6%.

Arithmetic coding is employed in image and video coding schemes to reduce the statistical redundancy of symbols emitted by coding engines. Most arithmetic coders proposed in the literature generate variable-length codes, i.e., they produce one long codeword of variable size. This requires renormalization operations to control the internal registers of the coder and the propagation of carry bits. This paper introduces an arithmetic coder that generates fixed-length codewords. The main advantage of the proposed coder is that it avoids renormalization procedures, which reduces computational complexity. Also, it uses a variable-size sliding window mechanism to estimate with high precision the probability of the emitted symbols. Experimental results indicate that the proposed coder achieves coding efficiency superior to those coders employed in JPEG2000 and HEVC while having lower computational costs. When integrated in a JPEG2000 implementation, the proposed coder achieves coding gains between 0.5 to 1 dB at medium and high rates, and speedups between 1.1 to 1.3 in the bitplane coding stage.

Context modeling is key in wavelet-based image coding schemes to achieve competitive coding performance. Commonly, context models are devised for a particular coding system and are employed for many different types of images. The aim of this work is to evaluate the suitability of three well-known context models for coding hyperspectral images, without focusing on a particular wavelet-based coding system. To do so, an entropy-based measure defined using the mechanisms utilized by modern image codecs is employed. The experimental results assess the appropriateness of the context models considering different coding rates and transform strategies. They reveal that some widely-used context models may not be as adequate as it is generally thought. The hints provided by this analysis may help to design simpler and more efficient wavelet-based codecs for hyperspectral images.

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.

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.

Embedded quantization is a mechanism through which image coding systems provide quality progressivity. Although the most common embedded quantization approach is to use uniform scalar dead zone quantization (USDQ) together with bit plane coding (BPC), recent work suggested that similar coding performance as that achieved with USDQ+BPC can be obtained with a general embedded quantization (GEQ) scheme than performs fewer quantization stages. Unfortunately, practical approaches of GEQ can not be implemented in bit plane coding engines without substantially modifying their structure. This work overcomes this drawback introducing a 2-step scalar dead zone quantization (2SDQ) scheme compatible with bit plane image coding that provides the same advantages of practical GEQ approaches. Herein, 2SDQ is introduced in the framework of JPEG2000 to demonstrate its viability and efficiency.

Computed Tomography (CT) devices irradiate a (human) body with controlled amounts of X-ray to produce an image where different substance (lung, tissue, vessels, etc.) can be identified unequivocally. Commonly, CT devices also capture areas that do not belong to the human body. Such areas are referred to as air pixels, and may contain imaging artifacts. The air pixels are irrelevant for the medical diagnostic and provoke an important degradation in coding efficiency. In order to improve coding performance, we propose an air filtering technique based on a thresholding in the wavelet domain. The thresholds are determined through the existing relation between wavelet coefficients and image samples, which can be expressed in terms of a probability function. The proposed scheme filters air pixels in the wavelet domain by removing coefficients that are below a given threshold. The thresholds are estimated for different resolution levels and subbands, obtaining a probability of 70% to correctly filter air pixels. Although the proposed technique introduces an slight distortion in terms of RMSE in the biological area, this distortion is negligible compared with the state-of-the-art HDCS filter. These results suggest that the rate-distortion coding performance of our proposal and HDCS outperform significantly the coding performance of JPEG2000. In addition, Table 1 provides the RMSE of the HDCS and our proposal when compared with the original image, indicating that our proposal introduces much less RMSE distortion.

Modern lossy image coding systems generate a quality progressive codestream that, truncated at increasing rates, produces an image with decreasing distortion. Quality progressivity is commonly provided by an embedded quantizer that employs uniform scalar deadzone quantization (USDQ) together with a bitplane coding strategy. This paper introduces a 2-step scalar deadzone quantization (2SDQ) scheme that achieves same coding performance as that of USDQ while reducing the coding passes and the emitted symbols of the bitplane coding engine. This serves to reduce the computational costs of the codec and/or to code high dynamic range images. The main insights behind 2SDQ are the use of two quantization step sizes that approximate wavelet coefficients with more or less precision depending on their density, and a rate-distortion optimization technique that adjusts the distortion decreases produced when coding 2SDQ indexes. The integration of 2SDQ in current codecs is straightforward. The applicability and efficiency of 2SDQ are demonstrated within the framework of JPEG2000.

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.

Abstract—The efficient transmission of large resolution im-ages and, in particular, the interactive transmission of images in a client-server scenario, is an important aspect for many applications. Among the current image compression standards, JPEG2000 excels for its interactive transmission capabilities. In general, three mechanisms are employed to optimize the trans-mission of images when using the JPEG2000 Interactive Protocol (JPIP): 1) packet re-sequencing at the server; 2) prefetching at the client; and 3) proxy servers along the network infrastructure. To avoid the congestion of the network, prefetching mechanisms are not commonly employed when many clients within a local area network (LAN) browse images from a remote server. Aimed to maximize the responsiveness of all the clients within a LAN, this work proposes the use of prefetching strategies at the proxy server –rather than at the clients. The main insight behind the proposed prefetching strategies is a user-navigation model and a semantic map that predict the future requests of the clients. Experimental results indicate that the introduction of these strategies into a JPIP proxy server enhances the browsing experience of the end-users notably. Index Terms—Interactive image transmission, JPEG2000, JPIP, prefetching strategies, user-navigation model, semantic map. I.

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.

The efficient transmission of large resolution images is a key aspect in many applications to minimize the transmission costs and to enhance the browsing experience. Among the currently available standards for the coding and transmission of imagery, JPEG2000 excels for its superior coding performance and advanced capabilities. The JPEG2000 Interactive Protocol (JPIP) minimizes the amount of information transmitted in a client-server scenario. Nonetheless, JPIP does not provide mechanisms to re-use data already delivered to clients browsing the same image within a local network. Common HTTP proxy servers are not able to understand the syntax of JPIP, thus specialized JPIP proxy servers are put in practice. This work improves the capabilities of traditional JPIP proxy servers by means of a user-navigation model that, together with prefetching strategies, allows the server to anticipate (potential) future requests of clients. Experimental evidence indicates that the introduction of the navigational model into a JPIP proxy server enhances the browsing experience notably.

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.

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.

This paper introduces a probability model for symbols emitted by bitplane image coding engines, which is conceived from a precise characterization of the signal produced by a wavelet transform. Main insights behind the proposed model are the estimation of the magnitude of wavelet coefficients as the arithmetic mean of its neighbors' magnitude (the so-called local average), and the assumption that emitted bits are under-complete representations of the underlying signal. The local average-based probability model is introduced in the framework of JPEG2000. While the resulting system is not JPEG2000 compatible, it preserves all features of the standard. Practical benefits of our model are enhanced coding efficiency, more opportunities for parallelism, and improved spatial scalability.

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.

Context-adaptive binary arithmetic coding (CABAC) is the most common strategy of current lossy, or lossy-to-lossless, image coding systems to diminish the statistical redundancy of symbols emitted by bitplane coding engines. Most coding schemes based on CABAC form contexts through the significance state of the neighbors of the currently coded coefficient, and adjust the probabilities of symbols as more data are coded. This work introduces a probabilities model for bitplane image coding that does not use context-adaptive coding. Modeling principles arise from the assumption that the magnitude of a transformed coefficient exhibits some correlation with the magnitude of its neighbors. Experimental results within the framework of JPEG2000 indicates 2% increment on coding efficiency.

Remote browsing of images is receiving much attention lately, mostly in niche applications like geographical information systems or in the telemedicine scenery. Interactive transmission of compressed images has been identified as the most competitive approach, being JPIP, JPEG2000 Interactive Protocol, a key-enabler for these situations. Also, it has been reported that JPIP Proxy Servers help to increase the transmission performance, and that prefetching strategies help to lower responsiveness time. In this paper we contribute a smart JPIP Proxy Server that, thanks to a prefetching strategy undertaken during idle transmission times, largely improves the viewing experience of the final user because of its anticipation of future navigation requests. Experimental results are reported for a remote sensing and a medical environment, respectively performing panning and zoom in, showing enhanced performance in both cases.

Context-adaptive binary arithmetic coding (CABAC) is a popular approach to diminish the statistical redundancy of symbols emitted by bitplane image coders. The main idea behind CABAC is to set up appropriate context models for coefficients, and to adapt probability estimates for each context to the nonstationary statistical behavior of symbols as more data are coded. This works introduces a mathematical model to determine probability estimates conceived from a characterization of the signal's nature within wavelet subbands. The proposed model assumes stationary statistical behavior for emitted symbols, thus the context-adaptive process carried out by CABAC is avoided. Experimental results in the framework of JPEG2000 suggest 2% increment on coding efficiency.

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.

Region of interest (ROI) coding is a feature of prominent image coding systems that enables the specification of different coding priorities to certain regions of the image. JPEG2000 provides ROI coding through two mechanisms: either modifying wavelet coefficients or using rate-distortion optimization techniques. Although ROI coding methods based on the modification of wavelet coefficients provide an excellent accuracy to delimit the ROI area (referred to as fine-grain accuracy), they significantly penalize the coding efficiency. On the other hand, methods based on rate-distortion optimization improve the coding efficiency but, so far, have not been able to achieve the intended fine-grain accuracy. This letter introduces two ROI coding methods that, using rate-distortion optimization techniques, achieve a fine-grain accuracy, comparable to the one obtained when wavelet coefficients are modified, and are competitive in terms of coding efficiency.

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.

Region Of Interest (ROI) coding is a mechanism deployed in several image coding systems to enable different degrees of coding priority to specific regions of the image. JPEG2000 standard provides two ROI coding methods. However, both of them are based in mechanisms that scale the quantized coefficients. This compels to encode the additional bit-planes needed for the scaling, causing a penalization in the overall coding performance, and the ROIs can not be modified without a complete re-encoding of the image. This paper introduces two ROI coding methods that use Rate-distortion optimization techniques toprioritize arbitrary ROIs over the rest of the image without penalizing the overall coding performance. Experimental results suggest that the proposed methods achieve a close tooptimal accuracy, improving the Implicit ROI coding method in terms of ROI rate distortion performance.

In this paper a modification on the file syntax of the CCSDS Recommendation for Image Data Coding (CCSDS-122-B-1) is presented. With respect to the Recommendation, the proposed modification provides, among others, scalability by quality, spatial location, resolution, and component, and also allows multicomponent data coding. In addition, experimental results show that our proposal produces a meaningful improvement over the performance of the Recommendation. Reported results for hyperspectral data are alsocompetitive with JPEG2000, the reference technique.

This work addresses the transmission of motion JPEG2000 over JPIP, proposing a Variable Bit-Rate (VBR) server-driven policy aimed to minimize the overall distortion of the transmitted frames. In addition, a block-wise strategy of quality layer truncation is introduced to enhance the quality of transmitted frames containing insufficient quality layers. We pose these matters in a general framework where the server is able to deliver video on-the-fly, without needing computationally intensive processing. Experimental results suggest that the proposed VBR policy effectively minimizes the overall distortion, and that the block-wise layer truncation can be jointly combined with the VBR policy to widely enhance the quality of the transmitted frames.

Quality scalability is a fundamental feature of JPEG2000, achieved through the use of quality layers that are optimally formed in the encoder by rate-distortion optimization techniques. Two points, related with the practical use of quality layers, may need to be addressed when dealing with JPEG2000 code-streams: 1) the lack of quality scalability of code-streams containing a single or few quality layers and 2) the rate-distortion optimality of windows of interest transmission. Addressing these two points, this paper proposes a mechanism that, without using quality layers, provides competitive quality scalability to code-streams. Its main key-feature is a novel characterization of the code-blocks rate-distortion contribution that does not use distortion measures based on the original image, or related with the encoding process. Evaluations against the common use of quality layers, and against a theoretical optimal coding performance when decoding windows of interest or when decoding the complete image area, suggest that the proposed method achieves close to optimal results.

Quality scalability is a fundamental feature of JPEG2000, achieved through the use of quality layers. Two points, related with the use of quality layers, may need to be addressed when dealing with JPEG-2000 code-streams: 1) the lack of quality scalability of single quality layer code-streams, and 2) the non rate-distortion optimality of windows of interest transmission. This paper introduces a new rate control method that can be applied to already encoded code-streams, addressing these two points. Its main key-feature is a novel characterization that can fairly estimate the rate-distortion slope of the coding passes of code-blocks without using any measure based on the original image or related with the encoding process. Experimental results suggest that the proposed method is able to supply quality scalability to already encoded code-streams achieving a near-optimal coding performance. The low computational costs of the method makes it suitable for use in interactive transmissions.

Geographic Information Systems (GIS) and Remote Sensing (RS) applications are becoming an important issue for the territorial management, governmental and research projects, and for many fields of our society. A characteristic of such applications is the displaying of successive layers of information that, in some cases, may overlap areas of the displayed images that are eventually never showed to the final user of the application. Even though these overlapped areas are of null interest, the coding of these images considers the complete area of the image, and thus the coding performance of the compression system is penalized. This paper introduces a novel use of the Region Of Interest (ROI) coding techniques to overcome the drawbacks of the map overlapping in GIS and RS applications. The proposed approach is based on a ROI coding method defined for the JPEG2000 standard that efficiently improves the coding performance and keeps JPEG2000 compliance.

This letter introduces a new rate control method devised to provide quality scalability to JPEG2000 codestreams containing a single or few quality layers. It is based on a Reverse subband scanning Order and a coding passes Concatenation (ROC) that does not use distortion measures based on the original image. The proposed ROC method allows a flexible rate control when the image has already been encoded, using negligible computational resources and obtaining the same efficiency as when using quality layers. Besides, the proposed ROC can be used in the encoding process to reduce the coder complexity, avoiding to encode unnecessary coding passes and achieving a competitive performance in terms of mean-square error (MSE)

The JPEG2000 standard does not specify how to perform the rate control strategy, needed either to achieve a target bitrate, or to construct quality layers. In this paper, an efficient rate control algorithm is described. It is based on the interleaving of coding passes and it has a low computational complexity. The proposed algorithm encodes only the coding passes included in the final codestream, and it fully avoids the need of any post compression rate distortion stage. Extensive experimental results show that the encoding performance of our method is competitive and similar to the optimal strategy. In addition, this proposal allows the extraction of a target bitrate from a codestream without the need of either knowing the original image, or of decoding any part of the codestream. The performance of this kind of extraction is equivalent to that obtained when decompressing a codestream organized in quality layers.

This study measures the effect of lossy image compression on the digital classification of crops and forest areas. A hybrid classification method using satellite images and other variables has been used. The results contribute interesting new data on the influence of compression on the quality of the produced cartography, both from a "by pixel" perspective and regarding the homogeneity of the obtained polygons. The classified area in classifications only carried out with radiometric variables or with NDVI and humidity (for crops) increases as image compression increases, although the increase is smaller for JPEG2000 formats and for crops. On the other hand, the classified area decreases in classifications which also take into account topoclimatic variables (for forests). Overall image accuracy diminishes at high compression ratios (CR), although the point of inflection occurs at different CR depending on the compression format. As a rule, the JPEG2000 format gives better results quantitatively for forests (accuracy and classified area) and visually (images with less "salt and pepper" effect) for both land covers.

This paper deals with the encoding of high resolution images for remote sensing and geographic information systems applications. We are currently investigating the suitability of several still image coding techniques for this kind of applications. We present results for an adapted and modified version of the CCSDS-ILDC technique. In addition to evaluate its compression factor and quality of recovery with respect to the latest still image coding standard JPEG2000, we also consider whether this technique may fulfill the particular functionalities requested by remote sensing users

With the aim of obtaining a valid compression method for remote sensing and geographic information systems, and because comparisons among the different available techniques are not always performed in a sufficient fair manner, we are currently developing a JAVA framework for evaluating several still image coding techniques. In addition to properly choose the best suitable technique according to compression factor and quality of recovery, it is expected that this setting will let us introduce the particular functionalities requested by this kind of applications.