Vector Quantization and Signal Compression

by Allen Gersho, Robert M. Gray

Overview

Herb Caen, a popular columnist for the San Francisco Chronicle, recently quoted a Voice of America press release as saying that it was reorganizing in order to "eliminate duplication and redundancy. " This quote both states a goal of data compression and illustrates its common need: the removal of duplication (or redundancy) can provide a more efficient representation of data and the quoted phrase is itself a candidate for such surgery. Not only can the number of words in the quote be reduced without losing information, but the statement would actually be enhanced by such compression since it will no longer exemplify the wrong that the policy is supposed to correct. Here compression can streamline the phrase and minimize the embarassment while improving the English style. Compression in general is intended to provide efficient representations of data while preserving the essential information contained in the data. This book is devoted to the theory and practice of signal compression, i. e. , data compression applied to signals such as speech, audio, images, and video signals (excluding other data types such as financial data or generalpurpose computer data). The emphasis is on the conversion of analog waveforms into efficient digital representations and on the compression of digital information into the fewest possible bits. Both operations should yield the highest possible reconstruction fidelity subject to constraints on the bit rate and implementation complexity.

Product Details

ISBN-13:	9780792391814
Publisher:	Springer US
Publication date:	11/30/1991
Series:	The Springer International Series in Engineering and Computer Science , #159
Edition description:	1992
Pages:	732
Product dimensions:	6.10(w) x 9.25(h) x 0.36(d)

Table of Contents

1 Introduction.- 1.1 Signals, Coding, and Compression.- 1.2 Optimality.- 1.3 How to Use this Book.- 1.4 Related Reading.- I Basic Tools.- 2 Random Processes and Linear Systems.- 3 Sampling.- 4 Linear Prediction.- II Scalar Coding.- 5 Scalar Quantization I.- 6 Scalar Quantization II.- 7 Predictive Quantization.- 8 Bit Allocation and Transform Coding.- 9 Entropy Coding.- III Vector Coding.- 10 Vector Quantization I.- 11 Vector Quantization II.- 12 Constrained Vector Quantization.- 13 Predictive Vector Quantization.- 14 Finite-State Vector Quantization.- 15 Tree and Trellis Encoding.- 16 Adaptive Vector Quantization.- 17 Variable Rate Vector Quantization.