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Genetics compression algorithms are the latest generation of lossless algorithms that compress data (typically sequences of nucleotides) using both conventional compression algorithms and genetic algorithms adapted to the specific datatype. In 2012, a team of scientists from Johns Hopkins University published a genetic compression algorithm ...
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.
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 ...
These two algorithms form the basis for many variations including LZW, LZSS, LZMA and others. Besides their academic influence, these algorithms formed the basis of several ubiquitous compression schemes, including GIF and the DEFLATE algorithm used in PNG and ZIP. They are both theoretically dictionary coders. LZ77 maintains a sliding window ...
Lossless compression of digitized data such as video, digitized film, and audio preserves all the information, but it does not generally achieve compression ratio much better than 2:1 because of the intrinsic entropy of the data. Compression algorithms which provide higher ratios either incur very large overheads or work only for specific data ...
Allows the user to adjust the balance between compression ratio and compression speed, without affecting the speed of decompression; LZO supports overlapping compression and in-place decompression. As a block compression algorithm, it compresses and decompresses blocks of data. Block size must be the same for compression and decompression.
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 ...
842, 8-4-2, or EFT is a data compression algorithm. It is a variation on Lempel–Ziv compression with a limited dictionary length. With typical data, 842 gives 80 to 90 percent of the compression of LZ77 with much faster throughput and less memory use. [1] Hardware implementations also provide minimal use of energy and minimal chip area.