Introduction to Information Theory and Data Compression

Book 3 of 7: Applied Mathematics

Hankerson, D.C.; Harris, Greg A.; Johnson Jr., Peter D.; Johnson, Peter D.; Hankerson, Darrel

ISBN 10: 0849339855 ISBN 13: 9780849339851

Published by Chapman and Hall; CRC, 1997

Language: English

Condition: Used - Good Hardcover

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About this Item

1997. First Edition. Chapman and Hall; CRC. Good. Hardcover. Text with scattered markings, underlining & formula notations throughout (some pages heavy). Name stamp of prior owner to endpages (Provenance copy, from the personal collection of former Computer, Engineering & Electrical Dept. Professor). Cover clean & bright, bit of edge, shelf wear. Binding solid & square. 332 pp. Illustrated. Dual-topic textbook covering information theory (entropy, channel capacity, Shannon's theorems, coding theory, error correction) and data compression (lossless methods: Huffman, arithmetic coding, Lempel-Ziv, adaptive methods, dictionary methods; lossy methods: JPEG, transform methods, wavelets). Topics can be studied independently. Includes elementary probability, Bernoulli trials, prefix-condition codes, Kraft-McMillan inequality, maximum likelihood decoding, probabilistic finite state automata, Gallager/Knuth algorithms. Ships quickly and with care.

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Bibliographic Details

Title

Introduction to Information Theory and Data Compression

Publisher

Chapman and Hall; CRC

Publication Date

1997

Language

English

ISBN 10

0849339855

ISBN 13

9780849339851

Binding

Hardcover

Edition

First Edition.

Condition

Good

Weight

49 ounces

Seller catalogs

COMPUTER SCIENCE & CYBER SEC

About this title

Synopsis

This book provides a basic introduction to both information theory and data compression. Although the two topics are related, this unique treatment allows readers to explore either topic independently. The authors' presentation of information theory is pitched at an elementary level, making the book less daunting than most other texts. The second edition includes a detailed history of information theory that provides a solid background for the quantification of the topic as developed by Claude Shannon. It also covers the information rate of a code and the trade-off between error correction and rate of information transmission, probabilistic finite state source automata, and wavelet methods.

Review

Statisticians, applied mathematicians, engineers, and computer scientists will find this well-written book useful.
-Journal of Statistical Computation and Simulation

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