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Introduction to Information Theory and Data Compression

Peter D. Johnson Jr. | Greg A. Harris | D.C. Hankerson
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A01=D.C. Hankerson
A01=Greg A. Harris
A01=Jr. Johnson
A01=Peter D. Johnson Jr.
adaptive compression techniques
advanced data compression algorithms
algorithm
alphabet
arithmetic
Arithmetic Coding
Author_D.C. Hankerson
Author_Greg A. Harris
Author_Jr. Johnson
Author_Peter D. Johnson Jr.
binary
Binary Expansion
Binary Symmetric Channel
Binary Words
Category=GPF
Category=UN
Category=UT
Code Word
Code Word Length
Compression Ratio
Cosine Transform
discrete channel analysis
encoding
Encoding Scheme
entropy calculation methods
eq_bestseller
eq_computing
eq_isMigrated=1
eq_isMigrated=2
eq_nobargain
eq_non-fiction
error correction codes
Finite Probability Space
finite state automata
Huffman Encoding
Huffman Tree
Huffman's Algorithm
huffmans
Huffman’s Algorithm
Input Alphabet
Input Frequencies
Interval Encoding
Leaf Nodes
letter
Output Alphabet
Prefix Condition
probabilistic source modeling
Relative Frequency
scheme
Sine Transforms
source
Source Alphabet
Source Letter
Source Word
Uniquely Decodable
words

Product details

  • ISBN 9781584883135
  • Weight: 680g
  • Dimensions: 152 x 229mm
  • Publication Date: 26 Feb 2003
  • Publisher: Taylor & Francis Inc
  • Publication City/Country: US
  • Product Form: Hardback
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Description

An effective blend of carefully explained theory and practical applications, this text imparts the fundamentals of both information theory and data compression. Although the two topics are related, this unique text allows either topic to be presented independently, and it was specifically designed so that the data compression section requires no prior knowledge of information theory.

The treatment of information theory, while theoretical and abstract, is quite elementary, making this text less daunting than many others. After presenting the fundamental definitions and results of the theory, the authors then apply the theory to memoryless, discrete channels with zeroth-order, one-state sources.

The chapters on data compression acquaint students with a myriad of lossless compression methods and then introduce two lossy compression methods. Students emerge from this study competent in a wide range of techniques. The authors' presentation is highly practical but includes some important proofs, either in the text or in the exercises, so instructors can, if they choose, place more emphasis on the mathematics.

Introduction to Information Theory and Data Compression, Second Edition is ideally suited for an upper-level or graduate course for students in mathematics, engineering, and computer science.

Features:

  • Expanded discussion of the historical and theoretical basis of information theory that builds a firm, intuitive grasp of the subject
  • Reorganization of theoretical results along with new exercises, ranging from the routine to the more difficult, that reinforce students' ability to apply the definitions and results in specific situations.
  • Simplified treatment of the algorithm(s) of Gallager and Knuth
  • Discussion of the information rate of a code and the trade-off between error correction and information rate
  • Treatment of probabilistic finite state source automata, including basic resul
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    About D.C. Hankerson | Greg A. Harris | Jr. Johnson | Peter D. Johnson Jr.
    Johnson, Jr.; Harris, Greg A.; Hankerson, D.C.

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