Amazon.com: Introduction to Information Theory and Data Compression (9780849339851): D.C. Hankerson, Greg A. Harris, Peter D. Johnson Jr., Peter D. Johnson, Darrel Hankerson: Books

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Introduction to Information Theory and Data Compression [Hardcover]

D.C. Hankerson (Author), Greg A. Harris (Author), Peter D. Johnson Jr. (Author), Peter D. Johnson (Author), Darrel Hankerson (Author)
4.8 out of 5 stars  See all reviews (4 customer reviews)


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Hardcover $102.43  
Hardcover, November 20, 1997 --  
There is a newer edition of this item:
Introduction to Information Theory and Data Compression, Second Edition (Discrete Mathematics and Its Applications) Introduction to Information Theory and Data Compression, Second Edition (Discrete Mathematics and Its Applications) 4.8 out of 5 stars (4)
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Book Description

November 20, 1997 0849339855 978-0849339851
An effective blend of carefully explained theory and practical applications, this book imparts the basics of both information theory and data compression. Although the two topics are related, this unique treatment allows readers to explore either topic independent of the other.

The authors' treatment of Information Theory, while theoretical and abstract, is pitched at an elementary level, less daunting than most other texts. After presenting the fundamental definitions and results of the theory, they then bring the theory to bear on noisy channels. A number of computational examples and exercises are included.

The Data Compression section acquaints readers with a myriad of lossless compression methods and introduces then to the weirder world of lossy compression and how one can proceed using various transforms. This section allows readers to emerge broadly conversant with and competent in a large range of techniques.

The unique, flexible presentation in Introduction to Information Theory and Data Compression provides the background needed to apply information theory to forming and answering theoretical questions in data compression. However, those curious about data compression, but with no interest in information theory and vice versa can profit equally from the book-as can those with a curiosity about the intriguing connections between the two areas.


Editorial Reviews

Review

Impressive...I spent time reading about a variety of topics for which I had very little background or knowledge...A valuable source of information...
- W.E. Schiesser, Lehigh University

Product Details

  • Hardcover: 332 pages
  • Publisher: CRC Press (November 20, 1997)
  • Language: English
  • ISBN-10: 0849339855
  • ISBN-13: 978-0849339851
  • Product Dimensions: 9.8 x 6.5 x 1 inches
  • Shipping Weight: 1.5 pounds
  • Average Customer Review: 4.8 out of 5 stars  See all reviews (4 customer reviews)
  • Amazon Best Sellers Rank: #2,710,528 in Books (See Top 100 in Books)

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Average Customer Review
4.8 out of 5 stars (4 customer reviews)
 
 
 
 
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5 of 5 people found the following review helpful:
5.0 out of 5 stars Well balanced!, January 30, 2003
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This review is from: Introduction to Information Theory and Data Compression (Hardcover)
The authors of this well balanced textbook succeed admirably well in teaching the subject to the union of students in math and in cs, and to engineers. The danger with subjects that cut accross fields is that they might appeal to the intersection of audiences involved rather than to the much larger union. The authors seem to be at home with all the types of readers, they realize that the lingo and the aim is different for the different and diverse groups of students. Indeed, the tools of information theory, data compression, and arithmetic coding are widely used in science. While the mathematical parts of the subject is old[Shannon, Kolmogorov..., measurements of information, entropy, channel capacity], the applications are still going strong, with new things coming out at a fast rate right up to the present. So the emphasis in the book on data and image compression is very appropriate. There is even a JPEGtool user's guide in the appendix.
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10 of 13 people found the following review helpful:
5.0 out of 5 stars Great Book on Compression, August 28, 2001
By 
This review is from: Introduction to Information Theory and Data Compression (Hardcover)
I am a gradute student in computer science and I have read a lot of books on this subject, including 1- Digital Image Compression, by Weidong Kou, 2- The Data Compression Book, by Mark Nelson, 3- JPEG Still Image Data Compression Standard, by Pennebaker. I have also browsed few other books on this subject, but one thing I can say for sure, that this is the best book ever written on the subject of Compression. It explains the information theory and data Compression in the best possible way, with best examples. Once read, you will never forget the algorithms. I just love this book, and read it every once in a while.
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0 of 4 people found the following review helpful:
4.0 out of 5 stars Mindboggling!, November 28, 2008
This book breaks ground even now in the 21st Century!

There's not a day that goes by - and I mean this literally -
that I do not use this book to break ground - and I mean that figuratively.
Wait - no, I mean that literally, too, come to think about it, if one
considers the as-yet-untilled fertile loam of consciousness the ground
through which this slender tome "turns the soil..." When seedling theory
raises a tentative monocotyledon into the light of possibility, it quickly
wellsprings into that cornucopia however you spell it of brilliant
whatchamacallit - information theory or simply the plain old everyday
garden variety GENIUS that we have come to expect of Peter D. Johnson,
boy inventor.

More, Professor Johnson, MORE!!!
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Inside This Book (learn more)
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First Sentence:
A finite probability space is a pair (S, P), in which S is a finite non-empty set and P : S [0,1] is a function satisfying. Read the first page
Key Phrases - Statistically Improbable Phrases (SIPs): (learn more)
optimal input frequencies, relative source frequencies, code word replacing, equal source frequencies, next sibling pair, recency rank, underflow count, new output alphabet, new input alphabet, relative input frequencies, maximum error probability, starter scheme, quantizing stage, digram frequencies, transition probabilities qij, luminance matrix, unmatched character, dyadic fractions, average code word length, prefix condition, interval encoding, source alphabet, probabilistic experiment, probability that heads, zigzag sequence
Key Phrases - Capitalized Phrases (CAPs): (learn more)
Theorem Suppose, Definition Suppose, Elementary Probability, Noiseless Coding Theorem, Kraft's Theorem, Example Suppose, Kraft's Inequality, New York, Claude Shannon, Lagrange Multiplier Theorem, New Underflow, Portable Network Graphics, Prove Theorem
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