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  2. Huffman coding - Wikipedia

    en.wikipedia.org/wiki/Huffman_coding

    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".

  3. Canonical Huffman code - Wikipedia

    en.wikipedia.org/wiki/Canonical_Huffman_code

    A = 11 B = 0 C = 101 D = 100 Here the letter A has been assigned 2 bits, B has 1 bit, and C and D both have 3 bits. To make the code a canonical Huffman code, the codes are renumbered. The bit lengths stay the same with the code book being sorted first by codeword length and secondly by alphabetical value of the letter: B = 0 A = 11 C = 101 D = 100

  4. Lossless compression - Wikipedia

    en.wikipedia.org/wiki/Lossless_compression

    The "trick" that allows lossless compression algorithms, used on the type of data they were designed for, to consistently compress such files to a shorter form is that the files the algorithms are designed to act on all have some form of easily modeled redundancy that the algorithm is designed to remove, and thus belong to the subset of files ...

  5. Lempel–Ziv–Stac - Wikipedia

    en.wikipedia.org/wiki/Lempel–Ziv–Stac

    Lempel–Ziv–Stac (LZS, or Stac compression or Stacker compression [1]) is a lossless data compression algorithm that uses a combination of the LZ77 sliding-window compression algorithm and fixed Huffman coding.

  6. Deflate - Wikipedia

    en.wikipedia.org/wiki/DEFLATE

    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:

  7. Lempel–Ziv–Welch - Wikipedia

    en.wikipedia.org/wiki/Lempel–Ziv–Welch

    The algorithm works best on data with repeated patterns, so the initial parts of a message see little compression. As the message grows, however, the compression ratio tends asymptotically to the maximum (i.e., the compression factor or ratio improves on an increasing curve, and not linearly, approaching a theoretical maximum inside a limited ...

  8. 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).

  9. Lempel–Ziv–Storer–Szymanski - Wikipedia

    en.wikipedia.org/wiki/Lempel–Ziv–Storer...

    Lempel–Ziv–Storer–Szymanski (LZSS) is a lossless data compression algorithm, a derivative of LZ77, that was created in 1982 by James A. Storer and Thomas Szymanski. LZSS was described in article "Data compression via textual substitution" published in Journal of the ACM (1982, pp. 928–951). [1] LZSS is a dictionary coding technique. It ...