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Shannon–Fano coding methods gave rise to the field of information theory and without its contributions, the world would not have any of the many successors; for example Huffman coding, or arithmetic coding.
Shannon–Fano codes are suboptimal in the sense that they do not always achieve the lowest possible expected codeword length, as Huffman coding does. [1] However, Shannon–Fano codes have an expected codeword length within 1 bit of optimal. Fano's method usually produces encoding with shorter expected lengths than Shannon's method.
In information theory, an entropy coding (or entropy encoding) is any lossless data compression method that attempts to approach the lower bound declared by Shannon's source coding theorem, which states that any lossless data compression method must have an expected code length greater than or equal to the entropy of the source.
In information theory, Shannon–Fano–Elias coding is a precursor to arithmetic coding, in which probabilities are used to determine codewords. [1] It is named for Claude Shannon , Robert Fano , and Peter Elias .
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".
David Albert Huffman (August 9, 1925 – October 7, 1999) was an American pioneer in computer science, known for his Huffman coding. [ 1 ] [ 2 ] He was also one of the pioneers in the field of mathematical origami .
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AppleFSCompression.framework (AFSC), the mechanism for quasi-transparent compression in HFS Plus and Apple File System, supports LZFSE and LZVN since OS X 10.9.. Apple's Disk Images framework has offered an LZFSE-based encoding called ULFO since Mac OS X 10.11, [9] accessible via hdiutil(1) [10] and some third-party image utilities.