Lossless Compression Handbook by Khalid Sayood PDF

By Khalid Sayood

ISBN-10: 0126208611

ISBN-13: 9780126208610

  • Never ahead of has the subject of lossless compression been so topical. that is why the new booklet of Lossless Compression instruction manual, is so well timed. It explains the method of compression and transmission of multimedia signs (images, textual content, audio and knowledge) in order that the decompressed or reconstructed facts precisely fit the unique. who's this publication for? Engineers, scientists and different execs who care for photo processing, sign processing, multimedia structures and instant know-how. it really is for a person who has an issue that calls for compression. - EMedia, The electronic Studio journal, might 2003 This ebook could be of curiosity to each engineer or researcher who's operating with coded information comparable to compressed pictures, textual content, or compressed data...Lossless Compression instruction manual is a needs to learn for all execs who're operating within the box of photo coding equivalent to JPEG 2000. every one bankruptcy has a variety of references that support the reader to discover extra approximately issues of curiosity. one other attention-grabbing characteristic is that authors of person chapters supply a observe on tips to pass additional on a selected subject on the finish of the bankruptcy. Pseudocode directory in many of the chapters permits speedier figuring out and implements numerous algorithms mentioned. total, this ebook is a superb creation and a convenient connection with the subject of lossless compression. i like to recommend this publication within the library of each operating person who's operating during this sector of lossless compression. - IEEE Circuits & units journal, Sept./Oct. 2004

List of Contributors

, Pages xvii-xviii

, Pages xix-xx
Chapter 1 - info thought in the back of resource Coding

, Pages 3-34
Chapter 2 - Complexity Measures

, Pages 35-52
Chapter three - common Codes

, Pages 55-78
Chapter four - Huffman Coding

, Pages 79-99
Chapter five - mathematics Coding

, Pages 101-152
Chapter 6 - Dictionary-Based info Compression: An Algorithmic Perspective

, Pages 153-167
Chapter 7 - Burrows—Wheeler Compression

, Pages 169-193
Chapter eight - Symbol–Ranking and ACB Compression

, Pages 195-204
Chapter nine - Lossless picture Compression

, Pages 207-226
Chapter 10 - textual content Compression

, Pages 227-245
Chapter eleven - Compression of Telemetry

, Pages 247-253
Chapter 12 - Lossless Compression of Audio Data

, Pages 255-267
Chapter thirteen - Algorithms for Delta Compression and distant dossier Synchronization

, Pages 269-289
Chapter 14 - Compression of Unicode Files

, Pages 291-297
Chapter 15 - JPEG-LS Lossless and close to Lossless photo Compression

, Pages 301-310
Chapter sixteen - The CCSDS Lossless facts Compression advice for area Applications

, Pages 311-326
Chapter 17 - Lossless Bilevel photograph Compression

, Pages 327-349
Chapter 18 - JPEG2000: hugely Scalable photograph Compression

, Pages 351-369,I-III
Chapter 19 - PNG Lossless photograph Compression

, Pages 371-390
Chapter 20 - Facsimile Compression

, Pages 391-402
Chapter 21 - Implementation of knowledge Compression

, Pages 405-446

, Pages 447-455

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Sample text

P m n , n to denote the probabilities. Remember that these are conditioned on the length of the output, so if A denotes the set of all length n strings, and P(x) denotes the probability of a string x in the original source S, and p(A) = Y~x~a P(x), then Pi,~ = (P(xi,n))/(P(A)). 1. With the above definitions, 0 < K ( S , ) - H ( S , ) < K(n) + c, where c is a constant that depends only on S (and in particular, not on n ). m Proof. Lower-bounding K (Sn) turns out to be easy. While Kolmogorov complexity gives much lower complexities for some strings than would be expected from Shannon Information Theory, this cannot allow Kolmogorov complexity (at least in the prefix-free variant) to beat the entropy bound on average!

T and t = 1O, 100, and 1000. 4, a subset of {0, 1}T. 0000000000 000000 0000000001 000001 0000000010 000010 1000000000 001010 0000000011 001011 9 1100000000 0000000111. 6 A Hamming Ball Suppose that we can afford M code sequences. This results in a rate R - [log 2 M ] / T . We then achieve the smallest possible error probability P, when we choose for the set ,4 the M most probable source sequences. Assume that 0 < 0 < ~. 1 If we now rank the source sequences according to their probability, the "first" sequence is 000.

The transmission of information. Bell Systems Technical Journal Vol. 17, pp. 535-564, July 1928. Jelinek, E, 1968. Probabilistic Information Theory, pp. 476--489, McGraw-Hill, New York. van Lint, J. , 1992. Introduction to Coding Theory. Springer-Verlag, Berlin/New York. , 1976. D. thesis. Stanford University, Palo Alto, CA. , 1976. Generalized Kraft inequality and arithmetic coding. IBM Journal of Research and Development, Vol. 20, p. 198. Shannon, C. , 1948. A mathematical theory of communication.

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Lossless Compression Handbook by Khalid Sayood

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