Huffman coding

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Huffman coding

(algorithm)
A data compression technique which varies the length of the encoded symbol in proportion to its information content, that is the more often a symbol or token is used, the shorter the binary string used to represent it in the compressed stream. Huffman codes can be properly decoded because they obey the prefix property, which means that no code can be a prefix of another code, and so the complete set of codes can be represented as a binary tree, known as a Huffman tree. Huffman coding was first described in a seminal paper by D.A. Huffman in 1952.

Huffman coding

A statistical compression method that converts characters into variable length bit strings. Most-frequently occurring characters are converted to shortest bit strings; least frequent, the longest. Compression takes two passes. The first pass analyzes a block of data and creates a tree model based on its contents. The second pass compresses the data via the model. Decompression decodes the variable length strings via the tree. See LZW.
References in periodicals archive ?
proposes a method of embedding data into JPEG bitstream by modifying Huffman code mapping.
From the construction, Algorithm 1 is associated with the Huffman code whose average length is the shortest.
If we extend now the words table to contain more than 30 words so the compression rate will be grater but in contrast the Huffman code will be larger.
Keywords: Source coding, prefix codes, Kraft's inequality, Shannon lower bound, data compression, Huffman code, Tunstall code, Khodak code, redundancy, distribution modulo 1, Mellin transform, complex asymptotics.
The compression scheme presented in this article is a variant of the word-based Huffman code [Bentley et al.
Huffman coding requires that one or more sets of Huffman code tables be specified by the application.
Second, based on Bits, the binary stream of secret message, we pick out a word from StackList whose Huffman code is the same as the beginning digits of Bits.
The Huffman code is the most widely-known prefix code and is minimal in that it provides the best compression of any prefix code applied to a fixed dictionary [5].
In [8], Huffman coefficient structure was divided into Huffman code and appended bits.
The thread zero puts the Huffman code of B in the end of the Huffman code of A, which constitutes the Huffman code of AC coefficient.
The CCITT facsimile coding standard [11] bases a Huffman code on the frequencies with which black and white runs of different lengths occur in sample documents.
The quantized frequency coefficients are replaced by Huffman codes, which provides lossless compression and hence it is named as "noiseless".