enow.com Web Search

Search results

  1. Results from the WOW.Com Content Network
  2. 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).

  3. Canonical Huffman code - Wikipedia

    en.wikipedia.org/wiki/Canonical_Huffman_code

    The normal Huffman coding algorithm assigns a variable length code to every symbol in the alphabet. 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.

  4. Package-merge algorithm - Wikipedia

    en.wikipedia.org/wiki/Package-merge_algorithm

    However, the original paper, "A fast algorithm for optimal length-limited Huffman codes", shows how this can be improved to O(nL)-time and O(n)-space. The idea is to run the algorithm a first time, only keeping enough data to be able to determine two equivalent subproblems that sum to half the size of the original problem.

  5. Deflate - Wikipedia

    en.wikipedia.org/wiki/DEFLATE

    Second and third bits: Encoding method used for this block type: 00: A stored (a.k.a. raw or literal) section, between 0 and 65,535 bytes in length; 01: A static Huffman compressed block, using a pre-agreed Huffman tree defined in the RFC; 10: A dynamic Huffman compressed block, complete with the Huffman table supplied; 11: Reserved—don't use.

  6. Entropy coding - Wikipedia

    en.wikipedia.org/wiki/Entropy_coding

    More precisely, the source coding theorem states that for any source distribution, the expected code length satisfies ⁡ [(())] ⁡ [⁡ (())], where is the number of symbols in a code word, is the coding function, is the number of symbols used to make output codes and is the probability of the source symbol. An entropy coding attempts to ...

  7. Modified Huffman coding - Wikipedia

    en.wikipedia.org/wiki/Modified_Huffman_coding

    Modified Huffman coding is used in fax machines to encode black-on-white images . It combines the variable-length codes of Huffman coding with the coding of repetitive data in run-length encoding . The basic Huffman coding provides a way to compress files with much repeating data, like a file containing text, where the alphabet letters are the ...

  8. Group 4 compression - Wikipedia

    en.wikipedia.org/wiki/Group_4_compression

    Group 4 compression is available in many proprietary image file formats as well as standardized formats such as TIFF, CALS, CIT (Intergraph Raster Type 24) and the PDF document format. G4 offers a small improvement over G3-2D by removing the end-of-line (EOL) codes. G3 and G4 compression both treat an image as a series of horizontal black ...

  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.