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Usually, video compression additionally employs lossy compression techniques like quantization that reduce aspects of the source data that are (more or less) irrelevant to the human visual perception by exploiting perceptual features of human vision. For example, small differences in color are more difficult to perceive than are changes in ...
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.
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 ...
Lossless data compression is used in many applications. For example, it is used in the ZIP file format and in the GNU tool gzip. It is also often used as a component within lossy data compression technologies (e.g. lossless mid/side joint stereo preprocessing by MP3 encoders and other lossy audio encoders). [2]
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]
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 ...
Pruning is a data compression technique in machine learning and search algorithms that reduces the size of decision trees by removing sections of the tree that are non-critical and redundant to classify instances.
The lossless quality of Burrows algorithm has provided for different algorithms with different purposes in mind. To name a few, Burrows–Wheeler transform is used in algorithms for sequence alignment, image compression, data compression, etc. The following is a compilation of some uses given to the Burrows–Wheeler Transform.