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 ?
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".
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.
iv) The number of possible sampling vectors is equal to the number of nodes in the Huffman tree, but only a subset of these vectors is used to determine a nonzero component of a given instance vector x.
From the construction of the Huffman tree in Section 3.
If the Huffman tree associated with x has no special node, then the average number of samples L needed to find the position of one nonzero component of x using Algorithm 1 is at most log n.
Since the Huffman tree can have at most one special node, we consider three cases:
1, build a Huffman tree with leaves {i}, i [member of] [[omega].
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.