Articles liés à Compression Algorithms for Real Programmers
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Synopsis
Compression Algorithms for Real Programmers Describes the basic algorithms and approaches for compressing information so you can create the smallest files possible. This title examines the classic algorithms like Huffman coding, arithmetic compression, and dictionary-based schemes. It also discusses the philosophy of compression to illustrate the underlying trade-offs in the algorithms. Full description
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Présentation de l'éditeur
In life, time is money, and on the Internet, the size of data is money. Small programs and small files take less disk space and cost less to send over the Internet. Compression Algorithms for Real Programmers describes the basic algorithms and approaches for compressing information so you can create the smallest files possible. These new algorithms are making it possible for people to take impossibly large audio and video files and compress them enough that they can flow over the Internet.
* Examines the classic algorithms like Huffman coding, arithmetic compression, and dictionary-based schemes in depth
* Describes the basic approaches used to squeeze audio and video signals by factors of as much as 100:1
* Discusses the philosophy of compression to illustrate the underlying trade-offs in the algorithms
* Explores the use of wavelets and other modeling techniques that use repetitive functions to squeeze audio and video
* Shows how programming solutions like Adobe PostScript can save space and make networks more efficient
* Describes new approaches using fractals and grammars just being explored by the compression community
* Shows how to extend the algorithms and use them for copyright protection
Biographie de l'auteur
Peter Wayner is a writer living in Baltimore and is the author of Digital Cash and Agents at Large (both Academic Press). His writings appear in numerous academic journals as well as the pages of more popular forums such as MacWorld and the New York Times. He has taught various computer science courses at Cornell University and Georgetown University.
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