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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 (CER) 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. [ABSTRACT FROM AUTHOR]

Analyses the effective elimination of error propagation effects on video compression, an error concealment algorithm for packet video was proposed. Adoption of this type of process in standard codecs; Details on the use of leaky prediction to assist in the solution to the problems in packet video; Methods used to evaluate the error propagation effects.

Image compression systems that exploit the properties of the human visual system have been studied extensively over the past few decades. For the JPEG2000 image compression standard, all previous methods that aim to optimize perceptual quality have considered the irreversible pipeline of the standard. In this work, we propose an approach for the reversible pipeline of the JPEG2000 standard. We introduce a new methodology to measure visibility of quantization errors when reversible color and wavelet transforms are employed. Incorporation of the visibility thresholds using this methodology into a JPEG2000 encoder enables creation of scalable codestreams that can provide both near-threshold and numerically lossless representations, which is desirable in applications where restoration of original image samples is required. Most importantly, this is the first work that quantifies the bitrate penalty incurred by the reversible transforms in near-threshold image compression compared to the irreversible transforms. • A method to measure visibility of quantization error in reversible JPEG2000 is proposed. • Near-threshold and lossless compression are enabled in a single scalable codestream. • The impact of the nonlinearities in the reversible pipeline of JPEG2000 on near-threshold compression performance is quantified. [ABSTRACT FROM AUTHOR]

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

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. [ABSTRACT FROM AUTHOR]

With the increasing utilization of medical imaging in clinical practice and the growing dimensions of data volumes generated by various medical imaging modalities, the distribution, storage, and management of digital medical image data sets requires data compression. Over the past few decades, several image compression standards have been proposed by international standardization organizations. This paper discusses the current status of these image compression standards in medical imaging applications together with some of the legal and regulatory issues surrounding the use of compression in medical settings. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM PUBLISHER]

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

Preclinical micro-computed tomography (microCT) images are of utility for 3D morphological bone evaluation, which is of great interest in cancer detection and treatment development. This work introduces a compression strategy for microCTs that allocates specific substances in different Volumes of Interest (VoIs). The allocation procedure is conducted by the Hounsfield scale. The VoIs are coded independently and then grouped in a single DICOM-compliant file. The proposed method permits the use of different codecs, identifies and transmit data corresponding to a particular substance in the compressed domain without decoding the volume(s), and allows the computation of the 3D morphometry without needing to store or transmit the whole image. The proposed approach reduces the transmitted data in more than 90% when the 3D morphometry evaluation is performed in high density and low density bone. This work can be easily extended to other imaging modalities and applications that work with the Hounsfield scale. [ABSTRACT FROM AUTHOR]

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

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. [ABSTRACT FROM AUTHOR]

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

The relation between the girth and the guaranteed error correction capability of γ-left-regular low-density parity-check (LDPC) codes when decoded using the bit flipping (serial and parallel) algorithms is investigated. A lower bound on the size of variable node sets which expand by a factor of at least 3γ/4 is found based on the Moore bound. This bound, combined with the well known expander based arguments, leads to a lower bound on the guaranteed error correction capability. The decoding failures of the bit flipping algorithms are characterized using the notions of trapping sets and fixed sets. The relation between fixed sets and a class of graphs known as cage graphs is studied. Upper bounds on the guaranteed error correction capability are then established based on the order of cage graphs. The results are extended to left-regular and right-uniform generalized LDPC codes. It is shown that this class of generalized LDPC codes can correct a linear number of worst case errors (in the code length) under the parallel bit flipping algorithm when the underlying Tanner graph is a good expander. A lower bound on the size of variable node sets which have the required expansion is established. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

This study evaluated the compressibility of multislice CT (MSCT) datasets and its dependence on (1) slice thickness and (2) the use of three-dimensional (3D) vs. 2D JPEG2000 compression methods. Five thoracic CT datasets were obtained using a 16-detector MSCT scanner with a collimation of 0.75 mm (120 kVp, 90 mAs) and reconstructed at five slice thicknesses from 0.75 to 10.0 mm. These datasets were irreversibly compressed using a standard 2D JPEG2000 encoder and a developmental 3D JPEG2000 algorithm based on Part 2 of the JPEG2000 standard. Compression ratios ranged from 4:1 to 64:1. Image distortion was computed utilizing peak signal-to-noise ratio (PSNR) and the Sarnoff JNDmetrix visual discrimination model. For 2D compression, the thinnest sections were substantially less compressible than thicker sections for the same level of image quality, particularly at higher compression ratios. Applying 3D compression yielded consistently higher image quality in most cases compared to 2D compression at the same ratios. The advantage of 3D compression increased for thinner slices and higher compression ratios. These results indicate that 3D JPEG2000 (Part 2) compression offers substantial advantages over the current 2D JPEG2000 standard, yielding better quantitative image quality at similar compression ratios or comparable image quality at higher compression ratios. [Copyright &y& Elsevier]

With increasing utilization of medical imaging in clinical practice and the growing dimensions of data volumes generated by various medical imaging modalities, the distribution, storage, and management of digital medical image data sets requires data compression. Over the past few decades, several image compression standards have been proposed by international standardization organizations. This paper discusses the current status of these image compression standards in medical imaging applications together with some of the legal and regulatory issues surrounding the use of compression in medical settings.

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

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.

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.

We consider a network of imaging sensors. We address the problem of energy-efficient communication of the measurements of the sensors. A novel algorithm is presented for the purpose of exploiting intersensor and intrasensor correlation, which is inherent in a network of imaging sensors. The collaborative algorithm is used in conjunction with a cooperative multihop routing strategy to maximize the lifetime of the network. The algorithm is demonstrated to achieve an average gain in the lifetime as high as 3.2 over previous methods.

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.

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

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.

We develop novel methods for compressing volumetric imagery that has been generated by single-platform (mobile) range sensors. We exploit the correlation structure inherent in multiple views in order to improve compression efficiency. We show that, for lossless compression, three-dimensional volumes compress more efficiently than two-dimensional (2D) images by a factor of 60%. Furthermore, our error metric for lossy compression suggests that accumulating more than nine range images in one volume before compression yields as much as a 99% improvement in compression performance over 2D compression.