Vector Quantization and Signal Compression

Allen Gersho (u. a.)

ISBN 10: 1461366127 / ISBN 13: 9781461366126

Published by Springer, 2012

About this Item

Vector Quantization and Signal Compression | Allen Gersho (u. a.) | Taschenbuch | xxiii | Englisch | 2012 | Springer | EAN 9781461366126 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu Print on Demand.

Seller Inventory # 105720953

Bibliographic Details

• Title: Vector Quantization and Signal Compression
• Publisher: Springer
• Publication Date: 2012
• Language: English
• ISBN 10: 1461366127
• ISBN 13: 9781461366126
• Binding: Taschenbuch
• Condition: Neu
• Dimensions: 235W x 155H x 41D millimeters 235 millimeters width by 155 millimeters height by 41 millimeters depth
• Weight: 1130 grams
• Seller catalogs:
  • Bücher

About this title

Synopsis

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.