Download A Concise Introduction to Data Compression by David Salomon PDF
By David Salomon
Compressing facts is an choice clearly chosen whilst confronted with difficulties of excessive bills or limited house. Written by means of a popular specialist within the box, this publication bargains readers a succinct, reader-friendly starting place to the manager methods, tools and strategies at the moment hired within the box of knowledge compression.
Part I provides the fundamental techniques to facts compression and describes a number of renowned strategies and strategies commonplace to compress facts. The reader discovers crucial recommendations, equivalent to variable-length and prefix codes, statistical distributions and run-length encoding. half II then concentrates on complicated suggestions, similar to mathematics coding, orthogonal transforms, subband transforms and the Burrows-Wheeler transform.
• transparent review of the rules underlying this field
• Outlines the necessities of a number of the techniques to compressing data
• includes many studying aids similar to: bankruptcy introductions and summaries, chapter-end routines, finished word list, etc.
• presents numerous examples of significant compression algorithms
• a great introductory quantity to David Salomon’s fourth variation of knowledge Compression: the total Reference
Complete and transparent, this ebook is the proper source for undergraduates in machine technology and calls for at the least arithmetic. it's also excellent for readers with a easy wisdom of desktop technological know-how desirous to know about facts compression.
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Extra info for A Concise Introduction to Data Compression
The curve is deﬁned by the following steps: 0. H0 is a single point. 1. H1 consists of four copies of (the point) H0 , connected with three straight segments of length h at right angles to each other. 14a. 2. 14b). Again there are four possible orientations of H2 , and the one shown is #2. It is constructed of orientations 1223 of H1 , connected by segments that go to the right, up, and to the left. 14d. 14c. The particular curve shown is orientation 1223 of H2 . 14: Hilbert Curves of Orders 1, 2, and 3 and Construction Rules.
It is constructed of orientations 1223 of H1 , connected by segments that go to the right, up, and to the left. 14d. 14c. The particular curve shown is orientation 1223 of H2 . 14: Hilbert Curves of Orders 1, 2, and 3 and Construction Rules. A chess board has 64 squares. Given a set of 32 cardboard rectangles, each covering two adjacent squares, we can easily use them to cover the entire board. We now remove two diagonally-opposite squares from the chess board, leaving 62 squares. Can they be covered by 31 cardboard rectangles?
Correlated data symbols such as the pixels of an image or the audio samples of a sound ﬁle, can be transformed to representations where they require fewer bits. This sounds like getting something for nothing, but in fact there is a price to pay. The transformed items (transform coeﬃcients) are decorrelated. Such a transform already achieves some degree of compression, but more can be obtained if lossy compression is Chapter Summary 59 an option. The transform coeﬃcients can be quantized, a process that results in small integers (which can be encoded with variable-length codes) and possibly also in runs of zeros (which can be compressed with RLE).