Overview
- Authors:
-
-
Allen Gersho
-
University of California, Santa Barbara, USA
-
Robert M. Gray
-
Stanford University, USA
Access this book
Other ways to access
Table of contents (17 chapters)
-
Front Matter
Pages i-xxii
-
Introduction
-
-
- Allen Gersho, Robert M. Gray
Pages 1-13
-
Basic Tools
-
-
- Allen Gersho, Robert M. Gray
Pages 17-47
-
- Allen Gersho, Robert M. Gray
Pages 49-81
-
- Allen Gersho, Robert M. Gray
Pages 83-129
-
Scalar Coding
-
Front Matter
Pages 131-131
-
- Allen Gersho, Robert M. Gray
Pages 133-172
-
- Allen Gersho, Robert M. Gray
Pages 173-202
-
- Allen Gersho, Robert M. Gray
Pages 203-223
-
- Allen Gersho, Robert M. Gray
Pages 225-257
-
- Allen Gersho, Robert M. Gray
Pages 259-305
-
Vector Coding
-
Front Matter
Pages 307-307
-
- Allen Gersho, Robert M. Gray
Pages 309-343
-
- Allen Gersho, Robert M. Gray
Pages 345-405
-
- Allen Gersho, Robert M. Gray
Pages 407-485
-
- Allen Gersho, Robert M. Gray
Pages 487-517
-
- Allen Gersho, Robert M. Gray
Pages 519-553
-
- Allen Gersho, Robert M. Gray
Pages 555-586
About this book
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 informa tion, 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 em barassment 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 general purpose 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.
Authors and Affiliations
-
University of California, Santa Barbara, USA
Allen Gersho
-
Stanford University, USA
Robert M. Gray