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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 ...
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 ...
In both lossy and lossless compression, information redundancy is reduced, using methods such as coding, quantization, DCT and linear prediction to reduce the amount of information used to represent the uncompressed data. Lossy audio compression algorithms provide higher compression and are used in numerous audio applications including Vorbis ...
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 ...
Published research on this family of algorithms can be found as far back as the mid-1980s. Software implementations were not popular until the early 1990s because PPM algorithms require a significant amount of RAM. Recent PPM implementations are among the best-performing lossless compression programs for natural language text.
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".
Lempel–Ziv–Markov chain algorithm; Lempel–Ziv–Oberhumer; Lempel–Ziv–Stac; Lempel–Ziv–Storer–Szymanski; Lempel–Ziv–Welch; Levenshtein coding; Liblzg; Line spectral pairs; Linear predictive coding; Log area ratio; Lossless compression; Lossless compression benchmarks; Lossless predictive audio compression; Lossy compression ...
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.