Compression and Coding Algorithms

by Alistair Moffat, Andrew Turpin

Overview

Compression and Coding Algorithms describes in detail the coding mechanisms that are available for use in data compression systems. The well known Huffman coding technique is one mechanism, but there have been many others developed over the past few decades, and this book describes, explains and assesses them. People undertaking research of software development in the areas of compression and coding algorithms will find this book an indispensable reference. In particular, the careful and detailed description of algorithms and their implementation, plus accompanying pseudo-code that can be readily implemented on computer, make this book a definitive reference in an area currently without one.

Product Details

ISBN-13:	9780792376682
Publisher:	Springer US
Publication date:	03/31/2002
Series:	The Springer International Series in Engineering and Computer Science , #669
Edition description:	2002
Pages:	275
Product dimensions:	6.10(w) x 9.25(h) x 0.03(d)

Table of Contents

1 Data Compression Systems.- 1.1 Why compression?.- 1.2 Fundamental operations.- 1.3 Terminology.- 1.4 Related material.- 1.5 Analysis of algorithms.- 2 Fundamental Limits.- 2.1 Information content.- 2.2 Kraft inequality.- 2.3 Human compression.- 2.4 Mechanical compression systems.- 3 Static Codes.- 3.1 Unary and binary codes.- 3.2 Elias codes.- 3.3 Golomb and Rice codes.- 3.4 Interpolative coding.- 3.5 Making a choice.- 4 Minimum-Redundancy Coding.- 4.1 Shannon-Fano codes.- 4.2 Huffman coding.- 4.3 Canonical codes.- 4.4 Other decoding methods.- 4.5 Implementing Huffman’s algorithm.- 4.6 Natural probability distributions.- 4.7 Artificial probability distributions.- 4.8 Doing the housekeeping chores.- 4.9 Related material.- 5 Arithmetic Coding.- 5.1 Origins of arithmetic coding.- 5.2 Overview of arithmetic coding.- 5.3 Implementation of arithmetic coding.- 5.4 Variations.- 5.5 Binary arithmetic coding.- 5.6 Approximate arithmetic coding.- 5.7 Table-driven arithmetic coding.- 5.8 Related material.- 6 Adaptive Coding.- 6.1 Static and semi-static probability estimation.- 6.2 Adaptive probability estimation.- 6.3 Coping with novel symbols.- 6.4 Adaptive Huffman coding.- 6.5 Adaptive arithmetic coding.- 6.6 Maintaining cumulative statistics.- 6.7 Recency transformations.- 6.8 Splay tree coding.- 6.9 Structured arithmetic coding.- 6.10 Pseudo-adaptive coding.- 6.11 The Q-coder.- 6.12 Making a choice.- 7 Additional Constraints.- 7.1 Length-limited coding.- 7.2 Alphabetic coding.- 7.3 Alternative channel alphabets.- 7.4 Related material.- 8 Compression Systems.- 8.1 Sliding window compression.- 8.2 Prediction by partial matching.- 8.3 Burrows-Wheeler transform.- 8.4 Other compression systems.- 8.5 Lossy modeling.- 9 What Next?.- References.

Editorial Reviews

Moffat (U. of Melbourne) and Turpin (Curtin U. of Technology, Australia) examine a wide range of mechanisms that have been proposed for coding in data compression systems, covering some fifty years of methods and algorithms. The text emphasizes the practicalities of implementation and execution. It includes descriptions of recent improvements to widely-known methods, such as minimum-redundancy (Huffman) coding and arithmetic coding, and coding problems with additional constraints, such as length-limited, alphabetic, and unequal letter-cost coding. Also discussed are three state-of-the-art compression systems, including descriptions of the type of coders employed and the reasons for that choice. For graduate and advanced graduate students, researchers, and software engineers. Annotation c. Book News, Inc., Portland, OR (booknews.com)