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 ?
The quantized frequency coefficients are replaced by Huffman codes, which provides lossless compression and hence it is named as "noiseless".
Canonical Huffman codes are used to ease the encoding and decoding.
Three variations of Huffman table alignment were considered: single frequency and Huffman codes table for all axes, two tables (for main and transverse axes), and a table for each axis.
Key words and phrases : Sampling, Sparsity, Compressed Sampling, Huffman codes
We now turn our attention to fixed-to-variable length codes, in particular Shannon and Huffman codes.
He describes in detail cryptographic protocols, key management, message authentication, applications such as email and Internet security, new wrinkles in noncryptographic security issues such as hacking and cybercrime, and closes with very useful descriptions of information theory and coding including Huffman codes.
We now define the different types of Huffman codes used in this work, all of which adhere to the above points.
VLCs and VLIs both are codes with variable lengths, but VLIs are not Huffman codes.
The second level is intra block parallelism; it utilizes the fact that both run-length encoding (RLE), which compresses long sequences of zeros after quantization of APBT-transformed data, and lookup of Huffman codes can be done in parallel.
Tight upper bounds on the redundancy of Huffman codes.
An offsetting advantage of Huffman codes is that they are more robust.
We present a Pascal implementation of the one-pass algorithm for constructing dynamic Huffman codes that is described and analyzed in a companion paper [3].