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Algorithms are generally quite specifically tuned to a particular type of file: for example, lossless audio compression programs do not work well on text files, and vice versa. In particular, files of random data cannot be consistently compressed by any conceivable lossless data compression algorithm; indeed, this result is used to define the ...
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 ...
The LZ4 algorithm aims to provide a good trade-off between speed and compression ratio. Typically, it has a smaller (i.e., worse) compression ratio than the similar LZO algorithm, which in turn is worse than algorithms like DEFLATE. However, LZ4 compression speed is similar to LZO and several times faster than DEFLATE, while decompression speed ...
This algorithm uses a dictionary compression scheme somewhat similar to the LZ77 algorithm published by Abraham Lempel and Jacob Ziv in 1977 and features a high compression ratio (generally higher than bzip2) [2] [3] and a variable compression-dictionary size (up to 4 GB), [4] while still maintaining decompression speed similar to other ...
LZ77 algorithms achieve compression by replacing repeated occurrences of data with references to a single copy of that data existing earlier in the uncompressed data stream. A match is encoded by a pair of numbers called a length-distance pair , which is equivalent to the statement "each of the next length characters is equal to the characters ...
Golomb coding is a lossless data compression method using a family of data compression codes invented by Solomon W. Golomb in the 1960s. Alphabets following a geometric distribution will have a Golomb code as an optimal prefix code, [1] making Golomb coding highly suitable for situations in which the occurrence of small values in the input stream is significantly more likely than large values.
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".
Run-length encoding (RLE) is a form of lossless data compression in which runs of data (consecutive occurrences of the same data value) are stored as a single occurrence of that data value and a count of its consecutive occurrences, rather than as the original run. As an imaginary example of the concept, when encoding an image built up from ...