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 ?
Compared to tree structure traversal in general, Huffman tree is especially convenient for the application of this method since it is full binary (all internal nodes have exactly two child branches).
Obviously this binary tree can be constructed out of trees, which there must be a weighted path length of the smallest binary tree, the tree binary tree called Huffman tree (Huffman), also known as the optimal tree.
The pseudo code of the binary tree constructed as follows: void CreateHuffmanTree (HuffmanTree T ) {// Construct the Huffman tree, T [m-1] for its root an int i, p1, p2; InitHuffmanTree (T); // T-initialization InputWeight (T); // input the value of the leaves of the right to T [0.
Huffman coding, the Huffman tree is created, agreed to the left branch, said the character "0", the right branch of the character "a".
Consequently, a single tree is formed as a combination of different trees and is termed as Huffman tree.
The new notion of Huffman tree and Huffman sampling vectors are introduced in Section 3.
We define a Huffman tree to be a binary tree whose leaves are the sets {1},.
We first construct a Huffman tree associated with the random vector X.
The Huffman codeword assigned to each text word is a sequence of whole bytes, and the Huffman tree has degree either 128 (which we call "tagged Huffman code") or 256 (which we call "plain Huffman code"), instead of 2.
In this work the Huffman codeword assigned to each text word is a sequence of whole bytes, and the Huffman tree has degree either 128 (in this case the eighth bit is used as a special mark to aid the search) or 256, instead of 2.
Figure 3 shows a Huffman tree for the example dictionary.
It supports stored mode, and both static and dynamic Huffman trees, and achieves a data throughput in the range 500 Mbps to 5 Gbps with a 100 MHz clock, depending upon area constraints.