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In computer science and information theory, a Huffman code is a particular type of optimal prefix code that is commonly used for lossless data compression.The process of finding or using such a code is Huffman coding, an algorithm developed by David A. Huffman while he was a Sc.D. student at MIT, and published in the 1952 paper "A Method for the Construction of Minimum-Redundancy Codes".
In order for a symbol code scheme such as the Huffman code to be decompressed, the same model that the encoding algorithm used to compress the source data must be provided to the decoding algorithm so that it can use it to decompress the encoded data. In standard Huffman coding this model takes the form of a tree of variable-length codes, with ...
Alternatively, the output of the problem can be used as a Huffman code, a method for encoding + given values unambiguously by using variable-length sequences of binary values. In this interpretation, the code for a value is given by the sequence of left and right steps from a parent to the child on the path from the root to a leaf in the tree ...
Instructions to generate the necessary Huffman tree immediately follow the block header. The static Huffman option is used for short messages, where the fixed saving gained by omitting the tree outweighs the percentage compression loss due to using a non-optimal (thus, not technically Huffman) code. Compression is achieved through two steps:
Huffman coding is a more sophisticated technique for constructing variable-length prefix codes. The Huffman coding algorithm takes as input the frequencies that the code words should have, and constructs a prefix code that minimizes the weighted average of the code word lengths. (This is closely related to minimizing the entropy.)
Efficiency: By assigning shorter codes to frequently occurring symbols, Huffman coding reduces the average code length, resulting in efficient data representation and reduced storage requirements. Compatibility: Huffman coding is widely supported and can be seamlessly integrated into existing image compression standards and algorithms.
While Fano's Shannon–Fano tree is created by dividing from the root to the leaves, the Huffman algorithm works in the opposite direction, merging from the leaves to the root. Create a leaf node for each symbol and add it to a priority queue , using its frequency of occurrence as the priority.
Zstandard combines a dictionary-matching stage with a large search window and a fast entropy-coding stage. It uses both Huffman coding (used for entries in the Literals section) [15] and finite-state entropy (FSE) – a fast tabled version of ANS, tANS, used for entries in the Sequences section. Because of the way that FSE carries over state ...