Design and Analysis of Fast Text Compression Based on Quasi-Arithmetic Coding

We give a detailed algorithm for fast text compression. Our algorithm, related to the PPM method, simpli es the modeling phase by eliminating the escape mechanism and speeds up coding by using a combination of quasi-arithmetic coding and Rice coding. We provide details of the use of quasi-arithmetic code tables, and analyze their compression performance. Our Fast PPM method is shown experimentally to be almost twice as fast as the PPMC method, while giving comparable compression

Parallel Lossless Image Compression Using Huffman and Arithmetic Coding

We show that high-resolution images can be encoded and decoded e ciently in parallel. We present an algorithm based on the hierarchical MLP method, used either with Hu man coding or with a new variant of arithmetic coding called quasi-arithmetic coding. The coding step can be parallelized, even though the codes for di erent pixels are of di erent lengths; parallelization of the prediction and error modeling components is straightforward

Bitplane image coding with parallel coefficient processing

Image coding systems have been traditionally tailored for multiple instruction, multiple data (MIMD) computing. In general, they partition the (transformed) image in codeblocks that can be coded in the cores of MIMD-based processors. Each core executes a sequential flow of instructions to process the coefficients in the codeblock, independently and asynchronously from the others cores. Bitplane coding is a common strategy to code such data. Most of its mechanisms require sequential processing of the coefficients. The last years have seen the upraising of processing accelerators with enhanced computational performance and power efficiency whose architecture is mainly based on the single instruction, multiple data (SIMD) principle. SIMD computing refers to the execution of the same instruction to multiple data in a lockstep synchronous way. Unfortunately, current bitplane coding strategies cannot fully profit from such processors due to inherently sequential coding task. This paper presents bitplane image coding with parallel coefficient (BPC-PaCo) processing, a coding method that can process many coefficients within a codeblock in parallel and synchronously. To this end, the scanning order, the context formation, the probability model, and the arithmetic coder of the coding engine have been re-formulated. The experimental results suggest that the penalization in coding performance of BPC-PaCo with respect to the traditional strategies is almost negligible

A novel approach for the hardware implementation of a PPMC statistical data compressor

This thesis aims to understand how to design high-performance compression algorithms suitable for hardware implementation and to provide hardware support for an efficient compression algorithm. Lossless data compression techniques have been developed to exploit the available bandwidth of applications in data communications and computer systems by reducing the amount of data they transmit or store. As the amount of data to handle is ever increasing, traditional methods for compressing data become· insufficient. To overcome this problem, more powerful methods have been developed. Among those are the so-called statistical data compression methods that compress data based on their statistics. However, their high complexity and space requirements have prevented their hardware implementation and the full exploitation of their potential benefits. This thesis looks into the feasibility of the hardware implementation of one of these statistical data compression methods by exploring the potential for reorganising and restructuring the method for hardware implementation and investigating ways of achieving efficient and effective designs to achieve an efficient and cost-effective algorithm. [Continues.

Context-based compression algorithms for text and image data.

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

Design and Analysis of Fast Text Compression Based on Quasi-Arithmetic Coding

We give a detailed algorithm for fast text compression. Our algorithm, related to the PPM method, simplifies the modeling phase by eliminating the escape mechanism and speeds up coding by using a combination of quasi-arithmetic coding and Rice coding. We provide details of the use of quasi-arithmetic code tables, and analyze their compression performance. Our Fast PPM method is shown experimentally to be almost twice as fast as the PPMC method, while giving comparable compression

Design and Analysis of Fast Text Compression Based on Quasi-Arithmetic Coding

We give a detailed algorithm for fast text compression. Our algorithm, related to the PPM method, simplifies the modeling phase by eliminating the escape mechanism and speeds up coding by using a combination of quasi-arithmetic coding and Rice coding. We provide details of the use of quasi-arithmetic code tables, and analyze their compression performance. Our Fast PPM method is shown experimentally to be almost twice as fast as the PPMC method, while giving comparable compression. 1 Introduction For compression of text files, the best compression results from the use of high-order models in conjunction with statistical coding techniques. The best compression reported in the literature comes from the PPM (prediction by partial matching) method of Cleary and Witten [3]; the most widely used implementation is Moffat's PPMC. The PPM methods use adaptive context models with a fixed maximum order, and arithmetic coding for the coder. In this paper we show that we can obtain significantly fa..