Rate-distortion optimal fast thresholding with complete JPEG/MPEG decoder compatibility

Notre projet est d’analyser le bien-être psychologique des habitants de Macao. Nous décrivons dans cet article comment l’industrie du jeu affecte aujourd’hui leur vie familiale. Les méthodes employées comprennent une approche par informateur-clé. Nous avons procédé à une analyse textuelle qualitative d’entretiens semi-directifs qui a mis en évidence quatre domaines où les effets du jeu se font ressentir : le fonctionnement de la famille, les relations familiales, les enfants et les problèmes psycho-sociaux au sein de la famille. Bien que l’industrie du jeu ne soit pas l’unique facteur en cause, elle affecte directement et indirectement les familles et le bien-être psychologique des individus et des groupes

Rate-distortion optimal fast thresholding with complete JPEG/MPEG decoder compatibility

We show a rate-distortion optimal way to threshold or drop the DCT coefficients of the JPEG and MPEG compression standards. Our optimal algorithm uses a fast dynamic programming recursive structure. The primary advantage of our approach lies in its complete compatibility with standard JPEG and MPEG decoders

A Generic Psychovisual Error Threshold for the Quantization Table Generation on JPEG Image Compression

The quantization process is a main part of image compression to control visual quality and the bit rate of the image output. The JPEG quantization tables are obtained from a series of psychovisual experiments to determine a visual threshold. The visual threshold is useful in handling the intensity level of the colour image that can be perceived visually by the human visual system. This paper will investigate a psychovisual error threshold at DCT frequency on the grayscale image. The DCT coefficients are incremented one by one for each frequency order. Whereby, the contribution of DCT coefficients to the error reconstruction will be a primitive pyschovisual error. At certain threshold being set on this psychovisual error, the new quantization table can be generated. The experimental results show that the new quantization table from psychovisual error threshold for DCT basis functions gives better quality image at lower average bit length of Huffman code than standard JPEG image compression

Rate control and bit allocations for JPEG transcoding

Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2007.Includes bibliographical references (leaves 50-51).An image transcoder that produces a baseline JPEG file from a baseline JPEG input is developed. The goal is to produce a high quality image while accurately meeting a filesize target and keeping computational complexity-especially the memory usage and number of passes at the input image--low. Building upon the work of He and Mitra, the JPEG transcoder exploits a linear relationship between the number of zero-valued quantized DCT coefficients and the bitrate. Using this relationship and a histogram of coefficients, it is possible to determine an effective way to scale the quantization tables of an image to approach a target filesize. As the image is being transcoded, an intra-image process makes minor corrections, saving more bits as needed throughout the transcoding of the image. This intra-image process decrements specific coefficients, minimizing the change in value (and hence image quality) while maximizing the savings in bitrate. The result is a fast JPEG transcoder that reliably achieves a target filesize and preserves as much image quality as possible. The proposed transcoder and several variations were tested on a set of twenty-nine images that gave a fair representation of typical JPEG photos. The evaluation metric consisted of three parts: first, the accuracy and precision of the output filesize with respect to the target filesize; second, the PSNR of the output image with respect to the original image; and third, the subjective visual image quality.by Ricky D. Nguyen.M.Eng

DESIGN AND IMPLEMENTATION OF AN EFFICIENT IMAGE COMPRESSOR FOR WIRELESS CAPSULE ENDOSCOPY

Capsule endoscope (CE) is a diagnosis tool for gastrointestinal (GI) diseases. Area and power are the two important parameters for the components used in CE. To optimize these two parameters, an efficient image compressor is desired. The mage compressor should be able to sufficiently compress the captured images to save transmission power, retain reconstruction quality for accurate diagnosis and consumes small physical area. To meet all of the above mentioned conditions, we have studied several transform coding based lossy compression algorithms in this thesis. The core computation tool of these compressors is the Discrete Cosine Transform (DCT) kernel. The DCT accumulates the distributed energy of an image in a small centralized area and supports more compression with non-significant quality degradation. The conventional DCT requires complex floating point multiplication, which is not feasible for wireless capsule endoscopy (WCE) application because of its high implementation cost. So, an integer version of the DCT, known as iDCT, is used in this work. Several low complexity iDCTs along with different color space converters (such as, YUV, YEF, YCgCo) were combined to obtain the desired compression level. At the end a quantization stage is used in the proposed algorithm to achieve further compression. We have analyzed the endoscopic images and based on their properties, three quantization matrix sets have been proposed for three color planes. The algorithms are verified at both software (using MATLAB) and hardware (using HDL Verilog coding) levels. In the end, the performance of all the proposed schemes has been evaluated for optimal operation in WCE application

Low frequency coefficient restoration for image coding.

by Man-Ching Auyeung.Thesis (M.Phil.)--Chinese University of Hong Kong, 1997.Includes bibliographical references (leaves 86-93).Chapter 1 --- Introduction --- p.1Chapter 1.1 --- Transform coding and the JPEG scheme --- p.2Chapter 1.2 --- Motivation --- p.5Chapter 1.3 --- Thesis outline --- p.6Chapter 2 --- MED and DC Coefficient Restoration scheme --- p.8Chapter 2.1 --- Introduction --- p.8Chapter 2.2 --- MED and DC Coefficient Restoration scheme --- p.10Chapter 2.2.1 --- Definition --- p.10Chapter 2.2.2 --- Existing schemes --- p.11Chapter 2.3 --- DC Coefficient Restoration scheme using block selection scheme --- p.14Chapter 2.4 --- Joint optimization technique --- p.16Chapter 2.4.1 --- Lagrange multiplier method --- p.17Chapter 2.4.2 --- Algorithm description --- p.18Chapter 2.5 --- Experimental results --- p.20Chapter 2.6 --- Summary --- p.32Chapter 3 --- Low Frequency Walsh Transform Coefficient Restoration scheme --- p.34Chapter 3.1 --- Introduction --- p.34Chapter 3.2 --- Restoration of low frequency coefficient using Walsh transform --- p.35Chapter 3.3 --- Selection of quantization table optimized for Walsh transform --- p.37Chapter 3.3.1 --- Image model used --- p.39Chapter 3.3.2 --- Infinite uniform quantization --- p.40Chapter 3.3.3 --- Search for an optimized quantization matrix --- p.42Chapter 3.4 --- Walsh transform-based LFCR scheme --- p.44Chapter 3.5 --- Experimental results --- p.46Chapter 3.6 --- Summary --- p.56Chapter 4 --- Low Frequency DCT Coefficient Prediction --- p.57Chapter 4.1 --- Introduction --- p.57Chapter 4.2 --- Low Frequency Coefficient Prediction scheme with negligible side information --- p.58Chapter 4.2.1 --- Selection of threshold --- p.63Chapter 4.2.2 --- Representation of the AC component --- p.63Chapter 4.3 --- Experimental results --- p.67Chapter 4.4 --- Summary --- p.84Chapter 5 --- Conclusions --- p.86Appendix A --- p.89Bibliography --- p.9

Context Adaptive Space Quantization for Image Coding

One of the most widely used lossy image compression formats in the world is JPEG. It operates by splitting an image into blocks, applying a frequency transform to each block, quantizing each transformed block, and entropy coding the resulting quantized values. Its popularity is a results of its simple technical description and its ability to achieve very good compression ratios. Given the enormous popularity of JPEG, much work has been done over the past two decades on quantizer optimization. Early works focused on optimizing the table of quantizer step sizes in JPEG in an adaptive manner, yielding significant gains in rate-distortion (RD) performance when compared to using the sample quantization table provided in the JPEG standard; this type of quantizer optimization is referred to as hard decision quantization (HDQ). To address the problem of local adaptivity in JPEG, optimization of the quantized values themselves was then considered in addition to optimizing the quantization table; this type of optimization is referred to as soft decision quantization (SDQ). But even SDQ methods cannot fully overcome the problem of local adaptivity in JPEG; nonetheless, the results from SDQ optimization suggest that overcoming this problem has potentially significant gains in RD performance. In this thesis, we propose a new kind of quantization called context adaptive space quantization (CASQ), where each block in an image is quantized and subsequently entropy coded conditioned on a quantization context. This facilitates the use of different quantizers for different parts of an image. If an image contains regions of varying amounts of detail, for example, then those regions may be assigned different quantization contexts so that they may be quantized differently; then, quantizer optimization may be performed over local regions of an image rather than other the entire image at once. In some sense, CASQ provides the ability to overcome the problem of local adaptivity. We also formulate and solve the problem of quantizer optimization in both the HDQ and SDQ settings using CASQ. We then propose a practical image coder based on JPEG using CASQ optimization. With our coder, significant gains in RD performance are observed. On average, in the case of Huffman coding under HDQ we see a gain of 1.78 dB PSNR compared to baseline JPEG and 0.23 dB compared to the state-of-the-art method. In the worst cases, our image coder performs no worse than state-of-the-art methods. Furthermore, the additional computational complexity of our image coder when compared to baseline JPEG encoding without optimization is very small, on the order of 150 ms for a 2048 x 2560 image in the HDQ case and 4000 ms in the SDQ case