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Lossless compression is a class of data compression that allows the original data to be perfectly reconstructed from the compressed data with no loss of information. Lossless compression is possible because most real-world data exhibits statistical redundancy . [ 1 ]
FLAC (/ f l æ k /; Free Lossless Audio Codec) is an audio coding format for lossless compression of digital audio, developed by the Xiph.Org Foundation, and is also the name of the free software project producing the FLAC tools, the reference software package that includes a codec implementation.
Lempel–Ziv–Welch (LZW) is a universal lossless data compression algorithm created by Abraham Lempel, Jacob Ziv, and Terry Welch.It was published by Welch in 1984 as an improved implementation of the LZ78 algorithm published by Lempel and Ziv in 1978.
XZ Utils (previously LZMA Utils) is a set of free software command-line lossless data compressors, including the programs lzma and xz, for Unix-like operating systems and, from version 5.0 onwards, Microsoft Windows.
LZ77 and LZ78 are the two lossless data compression algorithms published in papers by Abraham Lempel and Jacob Ziv in 1977 [1] and 1978. [2] They are also known as Lempel-Ziv 1 (LZ1) and Lempel-Ziv 2 (LZ2) respectively. [3] These two algorithms form the basis for many variations including LZW, LZSS, LZMA and others.
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".
LZMA2 compression, which is an improved version of LZMA, [13] is now the default compression method for the .7z format, starting with version 9.30 on October 26, 2012. [14] The reference open source LZMA compression library was originally written in C++ but has been ported to ANSI C, C#, and Java. [11]
Compression ratios are around 50–60% of the original size, [48] which is similar to those for generic lossless data compression. Lossless codecs use curve fitting or linear prediction as a basis for estimating the signal. Parameters describing the estimation and the difference between the estimation and the actual signal are coded separately.
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