Vector Quantization and Signal Compression

Name: Vector Quantization and Signal Compression
Brand: Springer
SKU: 9780792391814
Price: 465.97 PLN
Availability: InStock

Autor: Allen Gersho, Robert M. Gray

Język:

Oprawa: Twarda

Wydawca: Springer

Dostępność: Dostępna u dostawcy

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Herb Caen, a popular columnist for the San Francisco Chronicle, recently quoted a Voice of America p...

Informacje o książce

Autor

Allen Gersho, Robert M. Gray

Język

Angielski

Oprawa

Książka - Twarda

Data wydania

1991

strony

732

EAN

9780792391814

ISBN

0792391810

Enbook ID

01398089

Wydawca

Springer

Waga

2720

Wymiary

155 x 235 x 46

Pełny opis

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.

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