Data Compression: The Complete Reference

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Springer Science & Business Media, Feb 26, 2004 - Computers - 899 pages
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Data compression is one of the most important fields and tools in modern computing. From archiving data, to CD ROMs, and from coding theory to image analysis, many facets of modern computing rely upon data compression.
Data Compression provides a comprehensive reference for the many different types and methods of compression. Included are a detailed and helpful taxonomy, analysis of most common methods, and discussions on the use and comparative benefits of methods and description of "how to" use them. The presentation is organized into the main branches of the field of data compression: run length encoding, statistical methods, dictionary-based methods, image compression, audio compression, and video compression. Detailed descriptions and explanations of the most well-known and frequently used compression methods are covered in a self-contained fashion, with an accessible style and technical level for specialists and nonspecialists. Topics and features: coverage of video compression, including MPEG-1 and H.261; thorough coverage of wavelets methods, including CWT, DWT, EZW and the new Lifting Scheme technique; complete audio compression; QM coder used in JPEG and JBIG, including new JPEG 200 standard; image transformations and detailed coverage of discrete cosine transform and Haar transform; coverage of EIDAC method for compressing simple images; prefix image compression; ACB and FHM curve compression; geometric compression and edgebreaker technique.
Data Compression provides an invaluable reference and guide for all computer scientists, computer engineers, electrical engineers, signal/image processing engineers and other scientists needing a comprehensive compilation for a broad range of compression methods.

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Book Implies Reading Unknown Language More Reliable Than Braille
This book contains statements that are by logical necessity discriminatory of the visually impaired and it shows up for multiple
editions from 1997-2010. Beginning in the early editions of this book "Data Compression: The Complete Reference," a common example that is thread throughout the editions is the coding of a message into Braille.
The examples continue uncorrected even in the fifth edition of this series, where this book is renamed, Handbook of Data Compression, by David Salomon and Giovanni Motta.
Is Braille a lossy data compression? In this book you can read the author's counting argument model (Data Compression: The Complete Reference any edition, Section 1.1.1 - Braille) explaining Braille as a lossy data compression method and using examples that are (by logical necessity) discriminatory of the visually impaired. The author's choice of examples imply that given a choice between a sighted individual reading in an unknown language and a visually impaired individual reading Braille the most credibility for accuracy of information should be given to the sighted person.
Beneath are two comparisons where I synthesize an allegory from ideas in two continuous paragraphs Data Compression: The Complete Reference any edition, Section 1.1.1 - Braille :
(1) A sighted individual reads a newspaper in a language that is not known by them and that sighted individual is intelligent enough to understand "most of the news." (Data Compression: The Complete Reference any edition, Section 1.1.1 - Braille, exercise box #1.)
(2) A visually impaired individual reads the same newspaper printed in Braille and in the reader's native language. The visually impaired individual cannot distinguish between a few dots in Braille, so the visually impaired individual encounters "serious reading errors." (Data Compression: The Complete Reference any edition, Section 1.1.1 - Braille, immediately after exercise box #1)
In comparison, the ideas that they teach seem influenced by personal biases. Because all indications (including the advantage of language familiarity) would indicate that the visually impaired individual would also be intelligent enough to understand "most of the news."
Indeed, Motta and Salomon's incomprehensible model of recursion is explained by the counting argument models (counting redundancies as superfluous) which are espoused by Giovanni Motta with David Salomon in this book when the information altering paradigm is disclosed by the exercise for coding into Braille, which should be accomplished without losing information, but that is not apparent in Salomon`s and Motta's models. (Handbook Of Data Compression 5th Edition by David Salomon and Giovanni Motta, Exercise for Section 1.1.1 - Braille, on page 26, exercise box #1, when referring to information take note of the word "most" in the exercise.) So the connotations as well as the implications of using these books are far reaching and inaccurate. In another section about counting arguments in this series of books (first and second edition, 2.7 also as a online supplement for other editions) the author flashes a skewed definition of the logarithmic scale (2,4,8,...) and claims that the calculations (as in counting argument, 4-2=2) proves that files are random and therefore incompressible. It is apparent that the calculation the author calls a counting argument shows general logarithmic growth, however, the author's answer to the counting argument is two (2), but that answer is wrong. The correct answer is six (6) files, not two (2) files. And the reason is not logarithmic Mathematics. The reason is logical application and that is the same problem as the example for Braille. (c) Copyright 2010-2014

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About the author (2004)

David Salomon is retired professor emeritus of computer science from California State University, Northridge. He is author of numerous articles and books, including Data Compression, Third Edition, Guide to Data Compression Methods, and Data Privacy and Security.