One of the aims of the standardization committee has been the development of Part I, which could be used on a royalty- and fee-free basis. This is important for the standard to become widely accepted. The standardization process, which is coordinated by the JTCI/SC29/WG1 of the ISO/IEC has already produced the international standard (IS) for Part I. In this article the structure of Part I of the JPFG 2000 standard is presented and performance comparisons with established standards are reported. This article is intended to serve as a tutorial for the JPEG 2000 standard. The main application areas and their requirements are given. The architecture of the standard follows with the description of the tiling, multicomponent transformations, wavelet transforms, quantization and entropy coding. Some of the most significant features of the standard are presented, such as region-of-interest coding, scalability, visual weighting, error resilience and file format aspects. Finally, some comparative results are reported and the future parts of the standard are discussed.

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The JPEG 2000 still image compression standard

With the increasing use of multimedia technologies, image compression requires higher performance as well as new features. To address this need in the specific area of still image encoding, a new standard is currently being developed, the JPEG2000. It is not only intended to provide rate-distortion and subjective image quality performance superior to existing standards, but also to provide features and functionalities that current standards can either not address efficiently or in many cases cannot address at all. Lossless and lossy compression, embedded lossy to lossless coding, progressive transmission by pixel accuracy and by resolution, robustness to the presence of bit-errors and region-of-interest coding, are some representative features. It is interesting to note that JPEG2000 is being designed to address the requirements of a diversity of applications, e.g. Internet, color facsimile, printing, scanning, digital photography, remote sensing, mobile applications, medical imagery, digital library and E-commerce.

The JPEG2000 still image coding system: an overview

In this article we provide an overview of rate-distortion (R-D) based optimization techniques and their practical application to image and video coding. We begin with a short discussion of classical rate-distortion theory and then we show how in many practical coding scenarios, such as in standards-compliant coding environments, resource allocation can be put in an R-D framework. We then introduce two popular techniques for resource allocation, namely, Lagrangian optimization and dynamic programming. After a discussion of these techniques as well as some of their extensions, we conclude with a quick review of literature in these areas citing a number of applications related to image and video compression and transmission.

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Rate-distortion methods for image and video compression

In 1996, the JPEGcommittee began to investigate possibilities for a new still image compression standard to serve current and future applications. This initiative, which was named JPEG2000, has resulted in a comprehensive standard (ISO 154447ITU-T Recommendation T.800) that is being issued in six parts. Part 1, in the same vein as the JPEG baseline system, is aimed at minimal complexity and maximal interchange and was issued as an International Standard at the end of 2000. Parts 2–6 define extensions to both the compression technology and the file format and are currently in various stages of development. In this paper, a technical description of Part 1 of the JPEG2000 standard is provided, and the rationale behind the selected technologies is explained. Although the JPEG2000 standard only specifies the decoder and the codesteam syntax, the discussion will span both encoder and decoder issues to provide a better understanding of the standard in various applications. r 2002 Elsevier Science B.V. All rights reserved.

An overview of the JPEG2000 still image compression standard

We develop a probability model for natural images, based on empirical observation of their statistics in the wavelet transform domain. Pairs of wavelet coefficients, corresponding to basis functions at adjacent spatial locations, orientations, and scales, are found to be non-Gaussian in both their marginal and joint statistical properties. Specifically, their marginals are heavy-tailed, and although they are typically decorrelated, their magnitudes are highly correlated. We propose a Markov model that explains these dependencies using a linear predictor for magnitude coupled with both multiplicative and additive uncertainties, and show that it accounts for the statistics of a wide variety of images including photographic images, graphical images, and medical images. In order to directly demonstrate the power of the model, we construct an image coder called EPWIC (embedded predictive wavelet image coder), in which subband coefficients are encoded one bitplane at a time using a nonadaptive arithmetic encoder that utilizes conditional probabilities calculated from the model. Bitplanes are ordered using a greedy algorithm that considers the MSE reduction per encoded bit. The decoder uses the statistical model to predict coefficient values based on the bits it has received. Despite the simplicity of the model, the rate-distortion performance of the coder is roughly comparable to the best image coders in the literature.

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Image compression via joint statistical characterization in the wavelet domain

This paper addresses the problem of low memory wavelet image compression. While wavelet or subband coding of images has been shown to be superior to more traditional transform coding techniques, little attention has been paid until recently to the important issue of whether both the wavelet transforms and the subsequent coding can be implemented in low memory without significant loss in performance. We present a complete system to perform low memory wavelet image coding. Our approach is "line-based" in that the images are read line by line and only the minimum required number of lines is kept in memory. There are two main contributions of our work. First, we introduce a line-based approach for the implementation of the wavelet transform, which yields the same results as a "normal" implementation, but where, unlike prior work, we address memory issues arising from the need to synchronize encoder and decoder. Second, we propose a novel context-based encoder which requires no global information and stores only a local set of wavelet coefficients. This low memory coder achieves performance comparable to state of the art coders at a fraction of their memory utilization.

Line-based, reduced memory, wavelet image compression

In this work we propose a novel algorithm for wavelet based image compression with very low memory requirements. The wavelet transform is performed progressively and we only require that a reduced number of lines from the original image be stored at any given time. The result of the wavelet transform is the same as if we were operating on the whole image, the only difference being that the coefficients of different subbands are generated in an interleaved fashion. We begin encoding the (interleaved) wavelet coefficients as soon as they become available. We classify each new coefficient in one of several classes, each corresponding to a different probability model, with the models being adapted on the fly for each image. Our scheme is fully backward adaptive and it relies only on coefficients that have already been transmitted. Our experiments demonstrate that our coder is still very competitive with respect to similar state-of-the-art coders. It is noted that schemes based on zero trees or bit plane encoding basically require the whole image to be transformed (or else have to be implemented using tiling). The features of the algorithm make it well suited for a low memory mode coding within the emerging JPEG2000 standard.

Line based reduced memory, wavelet image compression

We present an adaptive image coding algorithm based on novel backward-adaptive quantization/classification techniques. We use a simple uniform scalar quantizer to quantize the image subbands. Our algorithm puts the coefficient into one of several classes depending on the values of neighboring previously quantized coefficients. These previously quantized coefficients form contexts which are used to characterize the subband data. To each context type corresponds a different probability model and thus each subband coefficient is compressed with an arithmetic coder having the appropriate model depending on that coefficient's neighborhood. We show how the context selection can be driven by rate-distortion criteria, by choosing the contexts in a way that the total distortion for a given bit rate is minimized. Moreover the probability models for each context are initialized/updated in a very efficient way so that practically no overhead information has to be sent to the decoder. Our results are comparable or in some cases better than the recent state of the art, with our algorithm being simpler than most of the published algorithms of comparable performance.

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Efficient context-based entropy coding for lossy wavelet image compression

To encode an unencoded block of a frame, a search window is defined within the frame. Each pixel disposed within the search window and disposed in the unencoded portion of the frame that is assigned a value. A difference is computed between the unencoded block and each possible block within the search window. The block having the smallest difference, together with this difference are used to encode the unencoded block.

Intra-prediction using intra-macroblock motion compensation

A subband decomposition of an image is performed by filtering the image in a progression of N line windows. In a first filter stage, vertical filters perform high and low pass filtering on each N line window. Then horizontal filtering is performed on the output of each vertical filter. The lowest subband provided by the filter stage is recursively decomposed by additional filter stages. Higher subbands of each filter stage are encoded and placed in an embedded bitstream. Block encoding can be performed in which a number of lines of each subband are coded as a plurality of blocks. The blocks of a subband are coded independently of each other. The block coding supports region-of-interest reconstruction.

Christos Chrysafis

Papers

Line-based, reduced memory, wavelet image compression

Line based reduced memory, wavelet image compression

Efficient context-based entropy coding for lossy wavelet image compression

Intra-prediction using intra-macroblock motion compensation

Efficient wavelet-based compression of large images