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Run-length Encoding
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 colored dots, the sequence "green green green green green green green green green" is shortened to "green x 9". This is most efficient on data that contains many such runs, for example, simple graphic images such as icons, line drawings, games, and animations. For files that do not have many runs, encoding them with RLE could increase the file size. RLE may also refer in particular to an early graphics file format supported by CompuServe for compressing black and white images, that was widely supplanted by their later Graphics Interchange Format (GIF). RLE also refers to a little-used image format in Windows 3.x that is saved with the file ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Lossless Data Compression
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. By contrast, lossy compression permits reconstruction only of an approximation of the original data, though usually with greatly improved compression rates (and therefore reduced media sizes). By operation of the pigeonhole principle, no lossless compression algorithm can efficiently compress all possible data. For this reason, many different algorithms exist that are designed either with a specific type of input data in mind or with specific assumptions about what kinds of redundancy the uncompressed data are likely to contain. Therefore, compression ratios tend to be stronger on human- and machine-readable documents and code in comparison to entropic binary data (random bytes). Lossless data compression is used in many ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Modified Huffman Coding
Modified Huffman coding is used in fax machines to encode black-on-white images (bitmaps). It combines the variable-length codes of Huffman coding with the coding of repetitive data in run-length encoding. The basic Huffman coding provides a way to compress files that have much repeating data, like a file containing text, where the alphabet letters are the repeating objects. However, a single scan line contains only two kinds of elements white pixels and black pixels which can be represented directly as a 0 and 1. This "alphabet" of only two symbols is too small to directly apply the Huffman coding. But if we first use run-length encoding, we can have more objects to encode. Here is an example taken from the article on run-length encoding Run-length encoding (RLE) is a form of lossless data compression in which ''runs'' of data (sequences in which the same data value occurs in many consecutive data elements) are stored as a single data value and count, rather than as the original ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Run-length Limited
Run-length limited or RLL coding is a line coding technique that is used to send arbitrary data over a communications channel with bandwidth limits. RLL codes are defined by four main parameters: ''m'', ''n'', ''d'', ''k''. The first two, ''m''/''n'', refer to the rate of the code, while the remaining two specify the minimal ''d'' and maximal ''k'' number of zeroes between consecutive ones. This is used in both telecommunication and storage systems that move a medium past a fixed recording head. Specifically, RLL bounds the length of stretches (runs) of repeated bits during which the signal does not change. If the runs are too long, clock recovery is difficult; if they are too short, the high frequencies might be attenuated by the communications channel. By modulating the data, RLL reduces the timing uncertainty in decoding the stored data, which would lead to the possible erroneous insertion or removal of bits when reading the data back. This mechanism ensures that the bound ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Recursive Indexing
{{no footnotes, date=June 2020 Recursive indexing is an algorithm used to represent large numberic values using members of a relatively small set. Recursive indexing writes the successive differences of the number after extracting the maximum value of the alphabet set from the number, and continuing recursively till the difference falls in the range of the set. Recursive indexing with a 2-letter alphabet is called unary code Unary may refer to: *Unary numeral system, the simplest numeral system to represent natural numbers *Unary function, a function that takes one argument; in computer science, a unary operator is a subset of unary function *Unary operation, a kind of .... Encoding To encode a number ''N'', keep reducing the maximum element of this set (''S''max) from ''N'' and output ''S''max for each such difference, stopping when the number lies in the half closed half open range – ''S''max). Example Let ''S'' = [0 1 2 3 4 … 10 be an 11-element se ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Burrows–Wheeler Transform
The Burrows–Wheeler transform (BWT, also called block-sorting compression) rearranges a character string into runs of similar characters. This is useful for compression, since it tends to be easy to compress a string that has runs of repeated characters by techniques such as move-to-front transform and run-length encoding. More importantly, the transformation is ''reversible'', without needing to store any additional data except the position of the first original character. The BWT is thus a "free" method of improving the efficiency of text compression algorithms, costing only some extra computation. The Burrows–Wheeler transform is an algorithm used to prepare data for use with data compression techniques such as bzip2. It was invented by Michael Burrows and David Wheeler in 1994 while Burrows was working at DEC Systems Research Center in Palo Alto, California. It is based on a previously unpublished transformation discovered by Wheeler in 1983. The algorithm can be imple ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Golomb Coding
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, 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. Rice coding Rice coding (invented by Robert F. Rice) denotes using a subset of the family of Golomb codes to produce a simpler (but possibly suboptimal) prefix code. Rice used this set of codes in an adaptive coding scheme; "Rice coding" can refer either to that adaptive scheme or to using that subset of Golomb codes. Whereas a Golomb code has a tunable parameter that can be any positive integer value, Rice codes are those in which the tunable parameter is a power of two. This makes Rice codes convenient for use on a computer since multiplication and division by 2 can be implemented more e ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Comparison Of Graphics File Formats
This is a comparison of image file formats (graphics file formats). This comparison primarily features file formats for 2D images. General Ownership of the format and related information. Technical details See also * List of codecs References {{Graphics file formats Graphics File Formats An Image file format is a file format for a digital image. There are many formats that can be used, such as JPEG, PNG, and GIF. Most formats up until 2022 were for storing 2D images, not 3D ones. The data stored in an image file format may be c ... * Graphics file ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Look-and-say Sequence
In mathematics, the look-and-say sequence is the integer sequence, sequence of integers beginning as follows: : 1, 11, 21, 1211, 111221, 312211, 13112221, 1113213211, 31131211131221, ... . To generate a member of the sequence from the previous member, read off the digits of the previous member, counting the number of digits in groups of the same digit. For example: * 1 is read off as "one 1" or 11. * 11 is read off as "two 1s" or 21. * 21 is read off as "one 2, one 1" or 1211. * 1211 is read off as "one 1, one 2, two 1s" or 111221. * 111221 is read off as "three 1s, two 2s, one 1" or 312211. The look-and-say sequence was analyzed by John Horton Conway, John Conway Reprinted as after he was introduced to it by one of his students at a party. The idea of the look-and-say sequence is similar to that of run-length encoding. If started with any digit ''d'' from 0 to 9 then ''d'' will remain indefinitely as the last digit of the sequence. For any ''d'' other than 1, the sequence ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Kolakoski Sequence
In mathematics, the Kolakoski sequence, sometimes also known as the Oldenburger–Kolakoski sequence, is an infinite sequence of symbols that is the sequence of run lengths in its own run-length encoding. It is named after the recreational mathematician William Kolakoski (1944–97) who described it in 1965, but it was previously discussed by Rufus Oldenburger in 1939. Definition The initial terms of the Kolakoski sequence are: :1,2,2,1,1,2,1,2,2,1,2,2,1,1,2,1,1,2,2,1,2,1,1,2,1,2,2,1,1,... Each symbol occurs in a "run" (a sequence of equal elements) of either one or two consecutive terms, and writing down the lengths of these runs gives exactly the same sequence: :1,2,2,1,1,2,1,2,2,1,2,2,1,1,2,1,1,2,2,1,2,1,1,2,1,2,2,1,1,2,1,1,2,1,2,2,1,2,2,1,1,2,1,2,2,... :1, 2 , 2 ,1,1, 2 ,1, 2 , 2 ,1, 2 , 2 ,1,1, 2 ,1,1, 2 , 2 ,1, 2 ,1,1, 2 ,1, 2 , 2&n ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
LZ77
LZ77 and LZ78 are the two lossless data compression algorithms published in papers by Abraham Lempel and Jacob Ziv in 1977 and 1978. They are also known as LZ1 and LZ2 respectively. 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 during compression. This was later shown to be equivalent to the ''explicit dictionary'' constructed by LZ78—however, they are only equivalent when the entire data is intended to be decompressed. Since LZ77 encodes and decodes from a sliding window over previously seen characters, decompression must always start at the beginning of the input. Conceptually, LZ78 decompression could allow random access to the input if the entire dictionary were known in ad ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
