A Concise Introduction to Data Compression by David Salomon

By David Salomon

Compressing info is an alternative certainly chosen whilst confronted with difficulties of excessive bills or limited house. Written by way of a well known specialist within the box, this publication bargains readers a succinct, reader-friendly starting place to the executive ways, equipment and methods presently hired within the box of information compression.

Part I provides the elemental methods to facts compression and describes a couple of renowned thoughts and techniques usual to compress info. The reader discovers crucial thoughts, reminiscent of variable-length and prefix codes, statistical distributions and run-length encoding. half II then concentrates on complex innovations, corresponding to mathematics coding, orthogonal transforms, subband transforms and the Burrows-Wheeler transform.

Features:
• transparent evaluation of the rules underlying this field
• Outlines the necessities of a few of the methods to compressing data
• comprises many studying aids resembling: bankruptcy introductions and summaries, chapter-end routines, entire word list, etc.
• presents a number of examples of vital compression algorithms
• a fantastic introductory quantity to David Salomon’s fourth version of information Compression: the full Reference

Complete and transparent, this publication is the precise source for undergraduates in laptop technology and calls for not less than arithmetic. it's also excellent for readers with a uncomplicated wisdom of desktop technology desirous to find out about facts compression.

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However, transforms play an important role in data compression, especially in the compression of images. A digital image can be compressed mainly because neighboring pixels tend to be similar; the individual pixels are correlated. An image transform takes advantage of this feature and converts correlated pixels to a representation where they are independent. Two types of transforms are employed in image compression, namely orthogonal and subband. They are described in detail in Chapter 5, while this section only illustrates the power of a transform by an example.

1 bit longer (per symbol) than an ideal entropy encoder, such as arithmetic coding (Chapter 4). This chapter describes the details of Huffman encoding and decoding and covers related topics such as the height of a Huffman code tree, canonical Huffman codes, and an adaptive Huffman algorithm. 4 illustrates an important application of the Huffman method to facsimile compression. David Huffman (1925–1999) Being originally from Ohio, it is no wonder that Huffman went to Ohio State University for his BS (in electrical engineering).

A little thinking, however, shows that because of the nature of the sliding window, the LZ77 method always compares the look-ahead buffer to the recently-input text in the search buffer and never to text that was input long ago (which has therefore been flushed out of the search buffer). Thus, the method implicitly assumes that patterns in the input data occur close together. Data that satisfies this assumption compresses well. The basic LZ77 method was improved in several ways by researchers and programmers during the 1980s and 1990s.

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