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Thesis (Ph. D.)--University of Texas at Austin, 1999. / Vita. Includes bibliographical references (leaves 202-214). Available also in a digital version from Dissertation Abstracts.

Thesis (M.S. in Computer Science) Naval Postgraduate School, March 1996. / Thesis advisor(s): Cynthia E. Irvine, Harold Fredricksen. "March 1996." Includes bibliography references (p. 37). Also available online.

Wong Ling. / Thesis (M.Phil.)--Chinese University of Hong Kong, 1997. / Includes bibliographical references (leaves 80-85). / ABSTRACT --- p.1 / Chapter 1. --- INTRODUCTION --- p.2 / Chapter 1.1 --- motivation --- p.4 / Chapter 1.2 --- Original Contributions --- p.5 / Chapter 1.3 --- thesis Structure --- p.5 / Chapter 2. --- BACKGROUND --- p.7 / Chapter 2.1 --- information theory --- p.7 / Chapter 2.2 --- early compression --- p.8 / Chapter 2.2.1 --- Some Source Codes --- p.10 / Chapter 2.2.1.1 --- Huffman Code --- p.10 / Chapter 2.2.1.2 --- Tutstall Code --- p.10 / Chapter 2.2.1.3 --- Arithmetic Code --- p.11 / Chapter 2.3 --- modern techniques for compression --- p.14 / Chapter 2.3.1 --- Statistical Modeling --- p.14 / Chapter 2.3.1.1 --- Context Modeling --- p.15 / Chapter 2.3.1.2 --- State Based Modeling --- p.17 / Chapter 2.3.2 --- Dictionary Based Compression --- p.17 / Chapter 2.3.2.1 --- LZ-compression --- p.19 / Chapter 2.3.3 --- Other Compression Techniques --- p.20 / Chapter 2.3.3.1 --- Block Sorting --- p.20 / Chapter 2.3.3.2 --- Context Tree Weighting --- p.21 / Chapter 3. --- SYMBOL REMAPPING --- p.22 / Chapter 3. 1 --- reviews on Block Sorting --- p.22 / Chapter 3.1.1 --- Forward Transformation --- p.23 / Chapter 3.1.2 --- Inverse Transformation --- p.24 / Chapter 3.2 --- Ordering Method --- p.25 / Chapter 3.3 --- discussions --- p.27 / Chapter 4. --- CONTENT PREDICTION --- p.29 / Chapter 4.1 --- Prediction and Ranking Schemes --- p.29 / Chapter 4.1.1 --- Content Predictor --- p.29 / Chapter 4.1.2 --- Ranking Techn ique --- p.30 / Chapter 4.2 --- Reviews on Context Sorting --- p.31 / Chapter 4.2.1 --- Context Sorting basis --- p.31 / Chapter 4.3 --- General Framework of Content Prediction --- p.31 / Chapter 4.3.1 --- A Baseline Version --- p.32 / Chapter 4.3.2 --- Context Length Merge --- p.34 / Chapter 4.4 --- Discussions --- p.36 / Chapter 5. --- BOUNDED-LENGTH BLOCK SORTING --- p.38 / Chapter 5.1 --- block sorting with bounded context length --- p.38 / Chapter 5.1.1 --- Forward Transformation --- p.38 / Chapter 5.1.2 --- Reverse Transformation --- p.39 / Chapter 5.2 --- Locally Adaptive Entropy Coding --- p.43 / Chapter 5.3 --- discussion --- p.45 / Chapter 6. --- CONTEXT CODING FOR IMAGE DATA --- p.47 / Chapter 6.1 --- Digital Images --- p.47 / Chapter 6.1.1 --- Redundancy --- p.48 / Chapter 6.2 --- model of a compression system --- p.49 / Chapter 6.2.1 --- Representation --- p.49 / Chapter 6.2.2 --- Quantization --- p.50 / Chapter 6.2.3 --- Lossless coding --- p.51 / Chapter 6.3 --- The Embedded Zerotree Wavelet Coding --- p.51 / Chapter 6.3.1 --- Simple Zerotree-like Implementation --- p.53 / Chapter 6.3.2 --- Analysis of Zerotree Coding --- p.54 / Chapter 6.3.2.1 --- Linkage between Coefficients --- p.55 / Chapter 6.3.2.2 --- Design of Uniform Threshold Quantizer with Dead Zone --- p.58 / Chapter 6.4 --- Extensions on Wavelet Coding --- p.59 / Chapter 6.4.1 --- Coefficients Scanning --- p.60 / Chapter 6.5 --- Discussions --- p.61 / Chapter 7. --- CONCLUSIONS --- p.63 / Chapter 7.1 --- Future Research --- p.64 / APPENDIX --- p.65 / Chapter A --- Lossless Compression Results --- p.65 / Chapter B --- Image Compression Standards --- p.72 / Chapter C --- human Visual System Characteristics --- p.75 / Chapter D --- Lossy Compression Results --- p.76 / COMPRESSION GALLERY --- p.77 / Context-based Wavelet Coding --- p.75 / RD-OPT-based jpeg Compression --- p.76 / SPIHT Wavelet Compression --- p.77 / REFERENCES --- p.80

A VLSI system for image compression based on two dimensional discrete cosine transform (2-D DCT) is designed and its performance is estimated. The focus is mainly on the reduction of power consumption and a reasonable speed. A 2-D DCT algorithm called row-column decomposition is chosen for the VLSI design of the system. Then a modified power saving architecture is proposed based on the property and purpose of image compression. Several methods, including the use of low power library cells and low voltage (Vdd=1.5v), are used to achieve the goal of power reduction. Techniques that reduces power, such as ordering of input signals and common term sharing, are applied to the design of the system. These techniques and methods span from algorithm, architecture, logic style and circuit. In addition to using standard cells, some custom cells are also created. The control, timing and synchronization circuitry is detailed in the design of the system. HSPICE simulation shows that the designed 2-D DCT system can operate at more than 20MHz for 8 by 8 image blocks using 1.2u CMOS technology. Based on the effective switched capacitances provided by library cell data sheets, power consumption performance is estimated. The system consumes about 17mW at the maximum speed and the specified supply voltage. Comparisons to other implementations show that the designed system exceeds in power performance. / Graduation date: 1996

Thesis (M.S.)--Baylor University, 2005. / Includes bibliographical references (p. 98).

Thesis (Ph. D.)--University of Texas at Austin, 2006. / Vita. Includes bibliographical references.

Thesis (Ph. D.)--West Virginia University, 2008. / Title from document title page. Document formatted into pages; contains xii, 102 p. : ill. (some col.). Includes abstract. Includes bibliographical references (p. 92-102).

Project report (M. Eng.)--Virginia Polytechnic Institute and State University, 1991. / Abstract. Includes bibliographical references (leaves 63-64). Also available via the Internet.

Thesis (Ph. D.)--University of Texas at Austin, 2008. / Vita. Includes bibliographical references.