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Dynamic Markov Compression
Dynamic Markov compression (DMC) is a lossless data compression algorithm developed by Gordon Cormack and Nigel Horspool.Gordon Cormack and Nigel Horspool, "Data Compression using Dynamic Markov Modelling", Computer Journal 30:6 (December 1987) It uses predictive arithmetic coding similar to prediction by partial matching (PPM), except that the input is predicted one bit at a time (rather than one byte at a time). DMC has a good compression ratio and moderate speed, similar to PPM, but requires somewhat more memory and is not widely implemented. Some recent implementations include the experimental compression programhookby Nania Francesco Antonio ocamydby Frank Schwellinger, and as a submodel in paq8l by Matt Mahoney. These are based on th1993 implementationin C by Gordon Cormack. Algorithm DMC predicts and codes one bit at a time. It differs from PPM in that it codes bits rather than bytes, and from context mixing algorithms such as PAQ in that there is only one context ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Data Compression
In information theory, data compression, source coding, or bit-rate reduction is the process of encoding information using fewer bits than the original representation. Any particular compression is either lossy or lossless. Lossless compression reduces bits by identifying and eliminating statistical redundancy. No information is lost in lossless compression. Lossy compression reduces bits by removing unnecessary or less important information. Typically, a device that performs data compression is referred to as an encoder, and one that performs the reversal of the process (decompression) as a decoder. The process of reducing the size of a data file is often referred to as data compression. In the context of data transmission, it is called source coding: encoding is done at the source of the data before it is stored or transmitted. Source coding should not be confused with channel coding, for error detection and correction or line coding, the means for mapping data onto a sig ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Algorithm
In mathematics and computer science, an algorithm () is a finite sequence of Rigour#Mathematics, mathematically rigorous instructions, typically used to solve a class of specific Computational problem, problems or to perform a computation. Algorithms are used as specifications for performing calculations and data processing. More advanced algorithms can use Conditional (computer programming), conditionals to divert the code execution through various routes (referred to as automated decision-making) and deduce valid inferences (referred to as automated reasoning). In contrast, a Heuristic (computer science), heuristic is an approach to solving problems without well-defined correct or optimal results.David A. Grossman, Ophir Frieder, ''Information Retrieval: Algorithms and Heuristics'', 2nd edition, 2004, For example, although social media recommender systems are commonly called "algorithms", they actually rely on heuristics as there is no truly "correct" recommendation. As an e ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Gordon Cormack
Gordon Villy Cormack is a professor emeritus in the David R. Cheriton School of Computer Science at the University of Waterloo and co-inventor of Dynamic Markov Compression. Cormack's research with Maura R. Grossman has been cited in cases of first impression in United States, Ireland, and (by reference) United Kingdom approving the use of technology-assisted review in civil litigation. Since 2001, he has been a program committee member of The Text Retrieval Conference (TREC). He was a coordinator of the TREC Total Recall Track (2015 - 2016), as well as the TREC Legal Track (2010 - 2011), and the TREC Spam Track (2005 - 2007). Cormack is past president of the Conference on Email and Anti-Spam. From 1997 through 2010, Cormack coached Waterloo's ACM International Collegiate Programming Contest team, qualifying for the World Finals every year, winning the World Championship in 1999, and the North American Championship in 1998 and 2000. Cormack was a member of the Internationa ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Nigel Horspool
R. Nigel Horspool is a retired professor of computer science, formerly of the University of Victoria. He invented the Boyer–Moore–Horspool algorithm, a fast string search algorithm adapted from the Boyer–Moore string-search algorithm. Horspool is co-inventor of dynamic Markov compression and was associate editor and then editor-at-large of the journal ''Software: Practice and Experience'' from 2007 to 2017. He is the author of ''C Programming in the Berkeley UNIX Environment''. Nigel Horspool is British by birth, but is now a citizen of Canada. After a public school education at Monmouth School, he studied at Pembroke College, Cambridge, where he received a BA in natural science, but specializing in theoretical physics, in 1969. After two years employment as an assembly language programmer on a partially successful air traffic control system project, he went to the University of Toronto for an MSc followed by a PhD in computer science. This was followed by seven years as a ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Arithmetic Coding
Arithmetic coding (AC) is a form of entropy encoding used in lossless data compression. Normally, a String (computer science), string of characters is represented using a fixed number of bits per character, as in the American Standard Code for Information Interchange, ASCII code. When a string is converted to arithmetic encoding, frequently used characters will be stored with fewer bits and not-so-frequently occurring characters will be stored with more bits, resulting in fewer bits used in total. Arithmetic coding differs from other forms of entropy encoding, such as Huffman coding, in that rather than separating the input into component symbols and replacing each with a code, arithmetic coding encodes the entire message into a single number, an arbitrary-precision arithmetic, arbitrary-precision fraction ''q'', where . It represents the current information as a range, defined by two numbers. A recent family of entropy coders called asymmetric numeral systems allows for faster imp ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Prediction By Partial Matching
Prediction by partial matching (PPM) is an adaptive statistical data compression technique based on context modeling and prediction. PPM models use a set of previous symbols in the uncompressed symbol stream to predict the next symbol in the stream. PPM algorithms can also be used to cluster data into predicted groupings in cluster analysis. Theory Predictions are usually reduced to symbol rankings. Each symbol (a letter, bit or any other amount of data) is ranked before it is compressed, and the ranking system determines the corresponding codeword (and therefore the compression rate). In many compression algorithms, the ranking is equivalent to probability mass function estimation. Given the previous letters (or given a context), each symbol is assigned with a probability. For instance, in arithmetic coding the symbols are ranked by their probabilities to appear after previous symbols, and the whole sequence is compressed into a single fraction that is computed according t ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Context Mixing
Context mixing is a type of data compression algorithm in which the next-symbol predictions of two or more statistical models are combined to yield a prediction that is often more accurate than any of the individual predictions. For example, one simple method (not necessarily the best) is to average the probabilities assigned by each model. The random forest is another method: it outputs the prediction that is the mode of the predictions output by individual models. Combining models is an active area of research in machine learning. The PAQ series of data compression programs use context mixing to assign probabilities to individual bits of the input. Application to Data Compression Suppose that we are given two conditional probabilities, P(X, A) and P(X, B), and we wish to estimate P(X, A,B), the probability of event X given both conditions A and B. There is insufficient information for probability theory to give a result. In fact, it is possible to construct scenarios in whic ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Claude Shannon
Claude Elwood Shannon (April 30, 1916 – February 24, 2001) was an American mathematician, electrical engineer, computer scientist, cryptographer and inventor known as the "father of information theory" and the man who laid the foundations of the Information Age. Shannon was the first to describe the use of Boolean algebra—essential to all digital electronic circuits—and helped found artificial intelligence (AI). Roboticist Rodney Brooks declared Shannon the 20th century engineer who contributed the most to 21st century technologies, and mathematician Solomon W. Golomb described his intellectual achievement as "one of the greatest of the twentieth century". At the University of Michigan, Shannon dual degreed, graduating with a Bachelor of Science in electrical engineering and another in mathematics, both in 1936. A 21-year-old master's degree student in electrical engineering at MIT, his thesis, "A Symbolic Analysis of Relay and Switching Circuits", demonstrated that electric ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Lossless Compression Algorithms
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 shrink the size of all possible data: Some data will get longer by at least one symbol or bit. Compression algorithms are usually effective for human- and machine-readable documents and cannot shrink the size of random data that contain no redundancy. 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. Lo ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Markov Models
In probability theory, a Markov model is a stochastic model used to Mathematical model, model pseudo-randomly changing systems. It is assumed that future states depend only on the current state, not on the events that occurred before it (that is, it assumes the Markov property). Generally, this assumption enables reasoning and computation with the model that would otherwise be Intractability (complexity), intractable. For this reason, in the fields of predictive modelling and probabilistic forecasting, it is desirable for a given model to exhibit the Markov property. Introduction Andrey Andreyevich Markov (14 June 1856 – 20 July 1922) was a Russian mathematician best known for his work on stochastic processes. A primary subject of his research later became known as the Markov chain. There are four common Markov models used in different situations, depending on whether every sequential state is observable or not, and whether the system is to be adjusted on the basis of observation ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
