We propose a new image multiresolution transform that is suited for both lossless (reversible) and lossy compression. The new transformation is similar to the subband decomposition, but can be computed with only integer addition and bit-shift operations. During its calculation the number of bits required to represent the transformed image is kept small through careful scaling and truncations. Numerical results show that the entropy obtained with the new transform is smaller than that obtained with predictive coding of similar complexity. In addition, we propose entropy-coding methods that exploit the multiresolution structure, and can efficiently compress the transformed image for progressive transmission (up to exact recovery). The lossless compression ratios are among the best in the literature, and simultaneously the rate vs. distortion performance is comparable to those of the most efficient lossy compression methods.

In this paper a new adaptive entropy coding scheme for video compression is presented. It utilizes an adaptive arithmetic coding technique to better match the first order entropy of the coded symbols and to keep track of nonstationary symbol statistics. In addition, remaining symbol redundancies will be exploited by context modeling to further reduce the bit-rate. A novel approach for coding of transform coefficients and a table look-up method for probability estimation and arithmetic coding is presented. Our new approach has been integrated in the current JVT test model (JM) to demonstrate the performance gain, and it was adopted as a part of the current JVT/H.26L draft.

We present a new image compression technique called "DjVu " that is specifically geared towards the compression of high-resolution, high-quality images of scanned documents in color. This enables fast transmission of document images over low-speed connections, while faithfully reproducing the visual aspect of the document, including color, fonts, pictures, and paper texture. The DjVu compressor separates the text and drawings, which needs a high spatial resolution, from the pictures and backgrounds, which are smoother and can be coded at a lower spatial resolution. Then, several novel techniques are used to maximize the compression ratio: the bi-level foreground image is encoded with AT&T's proposal to the new JBIG2 fax standard, and a new wavelet-based compression method is used for the backgrounds and pictures. Both techniques use a new adaptive binary arithmetic coder called the Z-coder. A typical magazine page in color at 300dpi can be compressed down to between 40 to 60 KB, approx...

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

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Our thesis is that high compression efficiency for text and images can be obtained by using sophisticated statistical compression techniques, and that greatly increased speed can be achieved at only a small cost in compression efficiency. Our emphasis is on elegant design and mathematical as well as empirical analysis. We analyze arithmetic coding as it is commonly implemented and show rigorously that almost no compression is lost in the implementation. We show that high-efficiency lossless compression of both text and grayscale images can be obtained by using appropriate models in conjunction with arithmetic coding. We introduce a four-component paradigm for lossless image compression and present two methods that give state of the art compression efficiency. In the text compression area, we give a small improvement on the preferred method in the literature. We show that we can often obtain significantly improved throughput at the cost of slightly reduced compression. The extra speed c...

Arithmetic coding, in conjunction with a suitable probabilistic model, can provide nearly optimal data compression. In this article we analyze the effect that the model and the particular implementation of arithmetic coding have on the code length obtained. Periodic scaling is often used in arithmetic coding implementations to reduce time and storage requirements; it also introduces a recency effect which can further affect compression. Our main contribution is introducing the concept of weighted entropy and using it to characterize in an elegant way the effect that periodic scaling has on the code length. We explain why and by how much scaling increases the code length for files with a homogeneous distribution of symbols, and we characterize the reduction in code length due to scaling for files exhibiting locality of reference. We also give a rigorous proof that the coding effects of rounding scaled weights, using integer arithmetic, and encoding end-of-file are negligible.

We provide a tutorial on arithmetic coding, showing how it provides nearly optimal data compression and how it can be matched with almost any probabilistic model. We indicate the main disadvantage of arithmetic coding, its slowness, and give the basis of a fast, space-efficient, approximate arithmetic coder with only minimal loss of compression efficiency. Our coder is based on the replacement of arithmetic by table lookups coupled with a new deterministic probability estimation scheme.

Much of recent progress in wavelet image compression came from better context modeling and entropy coding of quantized wavelet coefficients. In the past few months many papers on the subject were published [3, 4, 11]. They reported rate-distortion performance results that are among the best in the literature. Given seemingly ever smaller improvements on R-D performance with increasing sophistication and complexity of current R-D optimized quantizers and context models of wavelet coefficients, two tantalizing questions are whether there still exists some room for even higher coding efficiency of wavelet image coders, and if so, whether the improvement can be made with an embedded code stream. This paper will shed some lights on, and offer modestly encouraging answers to these questions. Index Terms. Wavelet image compression, entropy coding, context modeling of images, context quantization, embedded coding. EDICS Category: 1.1 1 Introduction In the author's opinion, adaptive context m...

A CMOS 64 # 64 pixel area image sensor chip using Sigma-Delta modulation at each pixel for A#D conversion is described. The image data output is digital. The chip was fabricated using a 1.2#mtwo layer metal single layer poly n-well CMOS process. Each pixel block consists of a phototransistor and 22 MOS transistors. Test results demonstrate a dynamic range potentially greater than 93dB, a signal to noise ratio #SNR# of up to 61dB, and dissipation of less than 1mW with a 5V power supply. 1 Boyd Fowler, Abbas El Gamal, and David X. D. Yang 2 Charge-coupled devices #CCD# are at present the most widely used technology for implementing area image sensors. CCD image sensors have their shortcomings, however. They su#er from low yields, they consume too muchpower #3#, and they are plagued with SNR limitations due to the shifting and detection of analog charge packets, and the fact that data is communicated o# chip in analog form. Several alternatives to CCD area image sensors that use st...