Download PDF by David Salomon: A Concise Introduction to Data Compression

By David Salomon

ISBN-10: 1848000715

ISBN-13: 9781848000711

Compressing facts is an choice evidently chosen while confronted with difficulties of excessive expenses or limited house. Written through a well known specialist within the box, this booklet deals readers a succinct, reader-friendly beginning to the executive methods, tools and strategies at the moment hired within the box of knowledge compression.

Part I offers the elemental techniques to facts compression and describes a number of renowned thoughts and strategies customary to compress facts. The reader discovers crucial techniques, resembling variable-length and prefix codes, statistical distributions and run-length encoding. half II then concentrates on complex ideas, reminiscent of mathematics coding, orthogonal transforms, subband transforms and the Burrows-Wheeler transform.

• transparent assessment of the foundations underlying this field
• Outlines the necessities of a number of the methods to compressing data
• comprises many studying aids equivalent to: bankruptcy introductions and summaries, chapter-end routines, complete word list, etc.
• presents numerous examples of vital compression algorithms
• a terrific introductory quantity to David Salomon’s fourth version of information Compression: the total Reference

Complete and transparent, this e-book is definitely the right source for undergraduates in desktop technological know-how and calls for at the very least arithmetic. it's also excellent for readers with a uncomplicated wisdom of machine technological know-how eager to know about facts compression.

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Extra info for A Concise Introduction to Data Compression

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Of special interest is the well-known Huffman algorithm for constructing a set of optimal prefix codes for data with known frequencies of occurrence. At a certain point he became interested in the mathematical properties of “zero curvature” surfaces, and developed this interest into techniques for folding paper into unusual sculptured shapes (the so-called computational origami). 1 Huffman Encoding The Huffman encoding algorithm starts by constructing a list of all the alphabet symbols in descending order of their probabilities.

Each vector should be run-length encoded separately. This means that any method for compressing grayscale images can be applied to color images as well. It is preferable to encode each row of the bitmap individually. Thus, if a row ends with four pixels of intensity 87 and the following row starts with nine such pixels, it is 44 1. Approaches to Compression better to write . . , 4, 87, 9, 87, . . on the output rather than . . , 13, 87, . .. It is even better to output the sequence . . , 4, 87, eol, 9, 87, .

It therefore makes sense to replace each by a variable-length code and write the codes on the output. Thus, RLE is normally just one step in a multistep compression algorithm that may include a transform, variable-length codes, and perhaps also quantization. 4) and the JPEG image compression 42 1. 6) employ specially-selected Huffman codes to write the run lengths on the output. The remainder of this section provides more information on the application of RLE to the compression of bi-level and grayscale images.

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A Concise Introduction to Data Compression by David Salomon

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