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  2. Shannon–Fano–Elias coding - Wikipedia

    en.wikipedia.org/wiki/ShannonFanoElias_coding

    Shannon–FanoElias coding produces a binary prefix code, allowing for direct decoding. Let bcode(x) be the rational number formed by adding a decimal point before a binary code. For example, if code(C) = 1010 then bcode(C) = 0.1010. For all x, if no y exists such that

  3. Shannon–Fano coding - Wikipedia

    en.wikipedia.org/wiki/ShannonFano_coding

    Unfortunately, Shannon–Fano coding does not always produce optimal prefix codes; the set of probabilities {0.35, 0.17, 0.17, 0.16, 0.15} is an example of one that will be assigned non-optimal codes by Shannon–Fano coding. Fano's version of Shannon–Fano coding is used in the IMPLODE compression method, which is part of the ZIP file format ...

  4. Elias coding - Wikipedia

    en.wikipedia.org/wiki/Elias_coding

    Elias coding is a term used for one of two types of lossless coding schemes used in digital communications: Shannon–FanoElias coding, a precursor to arithmetic coding, in which probabilities are used to determine codewords; Universal coding using one of Elias' three universal codes, each with predetermined codewords: Elias delta coding

  5. Shannon coding - Wikipedia

    en.wikipedia.org/wiki/Shannon_coding

    In the field of data compression, Shannon coding, named after its creator, Claude Shannon, is a lossless data compression technique for constructing a prefix code based on a set of symbols and their probabilities (estimated or measured).

  6. Elias gamma coding - Wikipedia

    en.wikipedia.org/wiki/Elias_gamma_coding

    Elias code or Elias gamma code is a universal code encoding positive integers developed by Peter Elias. [ 1 ] : 197, 199 It is used most commonly when coding integers whose upper-bound cannot be determined beforehand.

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  8. Entropy coding - Wikipedia

    en.wikipedia.org/wiki/Entropy_coding

    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.

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