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  2. Package-merge algorithm - Wikipedia

    en.wikipedia.org/wiki/Package-merge_algorithm

    The package-merge algorithm is an O(nL)-time algorithm for finding an optimal length-limited Huffman code for a given distribution on a given alphabet of size n, where no code word is longer than L. It is a greedy algorithm , and a generalization of Huffman's original algorithm .

  3. Huffman coding - Wikipedia

    en.wikipedia.org/wiki/Huffman_coding

    Huffman tree generated from the exact frequencies of the text "this is an example of a huffman tree". Encoding the sentence with this code requires 135 (or 147) bits, as opposed to 288 (or 180) bits if 36 characters of 8 (or 5) bits were used (This assumes that the code tree structure is known to the decoder and thus does not need to be counted as part of the transmitted information).

  4. Adaptive Huffman coding - Wikipedia

    en.wikipedia.org/wiki/Adaptive_Huffman_coding

    Adaptive Huffman coding (also called Dynamic Huffman coding) is an adaptive coding technique based on Huffman coding. It permits building the code as the symbols are being transmitted, having no initial knowledge of source distribution, that allows one-pass encoding and adaptation to changing conditions in data.

  5. Deflate - Wikipedia

    en.wikipedia.org/wiki/DEFLATE

    A match length code will always be followed by a distance code. Based on the distance code read, further "extra" bits may be read in order to produce the final distance. The distance tree contains space for 32 symbols: 0–3: distances 1–4; 4–5: distances 5–8, 1 extra bit; 6–7: distances 9–16, 2 extra bits; 8–9: distances 17–32, 3 ...

  6. Canonical Huffman code - Wikipedia

    en.wikipedia.org/wiki/Canonical_Huffman_code

    More frequently used symbols will be assigned a shorter code. For example, suppose we have the following non-canonical codebook: 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

  7. Tunstall coding - Wikipedia

    en.wikipedia.org/wiki/Tunstall_coding

    Unlike variable-length codes, which include Huffman and Lempel–Ziv coding, Tunstall coding is a code which maps source symbols to a fixed number of bits. [2]Both Tunstall codes and Lempel–Ziv codes represent variable-length words by fixed-length codes.

  8. File:Huffman coding example.svg - Wikipedia

    en.wikipedia.org/.../File:Huffman_coding_example.svg

    The picture is an example of Huffman coding. Colors make it clearer, but they are not necessary to understand it (according to Wikipedia's guidelines): probability is shown in red, binary code is shown in blue inside a yellow frame. For a more detailed description see below (I couldn't insert a table here). Date: 18 May 2007: Source: self-made

  9. Asymmetric numeral systems - Wikipedia

    en.wikipedia.org/wiki/Asymmetric_numeral_systems

    If symbols are assigned in ranges of lengths being powers of 2, we would get Huffman coding. For example, a->0, b->100, c->101, d->11 prefix code would be obtained for tANS with "aaaabcdd" symbol assignment. Example of generation of tANS tables for m = 3 size alphabet and L = 16 states, then applying them for stream decoding.