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  2. Shannon–Fano–Elias coding - Wikipedia

    en.wikipedia.org/wiki/Shannon–Fano–Elias_coding

    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 .

  3. 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).

  4. Shannon–Fano coding - Wikipedia

    en.wikipedia.org/wiki/Shannon–Fano_coding

    Fano's method usually produces encoding with shorter expected lengths than Shannon's method. However, Shannon's method is easier to analyse theoretically. Shannon–Fano coding should not be confused with Shannon–Fano–Elias coding (also known as Elias coding), the precursor to arithmetic coding.

  5. Elias omega coding - Wikipedia

    en.wikipedia.org/wiki/Elias_omega_coding

    If N = 1, stop; encoding is complete. Prepend the binary representation of N to the beginning of the code. This will be at least two bits, the first bit of which is a 1. Let N equal the number of bits just prepended, minus one. Return to Step 2 to prepend the encoding of the new N. To decode an Elias omega-encoded positive integer:

  6. List of algorithms - Wikipedia

    en.wikipedia.org/wiki/List_of_algorithms

    Shannon–Fano–Elias coding: precursor to arithmetic encoding [5] Entropy coding with known entropy characteristics. Golomb coding: form of entropy coding that is optimal for alphabets following geometric distributions; Rice coding: form of entropy coding that is optimal for alphabets following geometric distributions; Truncated binary encoding

  7. Universal code (data compression) - Wikipedia

    en.wikipedia.org/wiki/Universal_code_(data...

    Huffman coding and arithmetic coding (when they can be used) give at least as good, and often better compression than any universal code.. However, universal codes are useful when Huffman coding cannot be used — for example, when one does not know the exact probability of each message, but only knows the rankings of their probabilities.

  8. Prediction by partial matching - Wikipedia

    en.wikipedia.org/wiki/Prediction_by_partial_matching

    PPM compression implementations vary greatly in other details. The actual symbol selection is usually recorded using arithmetic coding, though it is also possible to use Huffman encoding or even some type of dictionary coding technique. The underlying model used in most PPM algorithms can also be extended to predict multiple symbols.

  9. Category:Data compression - Wikipedia

    en.wikipedia.org/wiki/Category:Data_compression

    Articles relating to data compression, the process of encoding information using fewer bits than the original representation. Subcategories This category has the following 9 subcategories, out of 9 total.