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Introduction to Data Compression
The most comprehensive, up-to-date introduction to data compression available
Khalid Sayood (Author)
9780128094747, Elsevier Science
Paperback, published 23 October 2017
790 pages
23.4 x 19 x 4.8 cm, 1.59 kg
Introduction to Data Compression, Fifth Edition, builds on the success of what is widely considered the best introduction and reference text on the art and science of data compression. Data compression techniques and technology are ever-evolving with new applications in image, speech, text, audio and video. This new edition includes all the latest developments in the field. Khalid Sayood provides an extensive introduction to the theory underlying today’s compression techniques, with detailed instruction for their applications using several examples to explain the concepts. Encompassing the entire field of data compression, the book includes lossless and lossy compression, Huffman coding, arithmetic coding, dictionary techniques, context based compression, and scalar and vector quantization. The book provides a comprehensive working knowledge of data compression, giving the reader the tools to develop a complete and concise compression package.
1. Introduction2. Lossless Compression3. Huffman Coding4. Arithmetic Coding5. Dictionary Techniques6. Context Based Compression7. Lossless Image Compression8. Lossy Coding9. Scalar Quantization10. Vector Quantization11. Differential Encoding12. Transforms, Subbands, and Wavelets13. Transform Coding14. Subband Coding15. Wavelet-Based Compression16. Audio Coding17. Analysis/Synthesis and Analysis by Synthesis Schemes18. Video Compression Appendix A: Probability and Random ProcessesAppendix B: A Brief Review of Matrix ConceptsAppendix C: The Root Lattices
Subject Areas: Signal processing [UYS], Data encryption [URY], Programming & scripting languages: general [UMX], Graphics programming [UML], Algorithms & data structures [UMB], Information technology: general issues [UB], Electricity, electromagnetism & magnetism [PHK], Information theory [GPF]
