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7z (file Format)
7z is a compressed archive file format that supports several different data compression, encryption and pre-processing algorithms. The 7z format initially appeared as implemented by the 7-Zip archiver. The 7-Zip program is publicly available under the terms of the GNU Lesser General Public License. The LZMA SDK 4.62 was placed in the public domain in December 2008. The latest stable version of 7-Zip and LZMA SDK is version 22.01. The official, informal 7z file format specification is distributed with 7-Zip's source code since 2015. The specification can be found in plain text format in the 'doc' sub-directory of the source code distribution. There have been additional third-party attempts at writing more concrete documentation based on the released code. Features and enhancements The 7z format provides the following main features: * Open, modular architecture that allows any compression, conversion, or encryption method to be stacked. * High compression ratios (dependin ... [...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 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 signal. ... [...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 ... [...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 of characters is represented using a fixed number of bits per character, as in the 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 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 implementations thanks to directly operating on a single natural number representing the current information., b ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Bzip
The Basic Leucine Zipper Domain (bZIP domain) is found in many DNA binding eukaryotic proteins. One part of the domain contains a region that mediates sequence specific DNA binding properties and the leucine zipper that is required to hold together (dimerize) two DNA binding regions. The DNA binding region comprises a number of basic amino acids such as arginine and lysine. Proteins containing this domain are transcription factors. bZIP transcription factors bZIP transcription factors are found in all eukaryotes and form one of the largest families of dimerizing TFs. An evolutionary study from 2008 revealed that 4 bZIP genes were encoded by the genome of the most recent common ancestor of all plants. Interactions between bZIP transcription factors are numerous and complex and play important roles in cancer development in epithelial tissues, steroid hormone synthesis by cells of endocrine tissues, factors affecting reproductive functions, and several other phenomena that affe ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Huffman Coding
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 proceeds by means of 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". The output from Huffman's algorithm can be viewed as a variable-length code table for encoding a source symbol (such as a character in a file). The algorithm derives this table from the estimated probability or frequency of occurrence (''weight'') for each possible value of the source symbol. As in other entropy encoding methods, more common symbols are generally represented using fewer bits than less common symbols. Huffman's method can be efficiently implemented, finding a code in time linear to the number of input weights if these weights are sorted. However, although opt ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Move To Front
The move-to-front (MTF) transform is an encoding of data (typically a stream of bytes) designed to improve the performance of entropy encoding techniques of compression. When efficiently implemented, it is fast enough that its benefits usually justify including it as an extra step in data compression algorithm. This algorithm was first published by B. Ryabko under the name of "book stack" in 1980. Subsequently, it was rediscovered by J.K. Bentley et al. in 1986, as attested in the explanatory note. The transform The main idea is that each symbol in the data is replaced by its index in the stack of “recently used symbols”. For example, long sequences of identical symbols are replaced by as many zeroes, whereas when a symbol that has not been used in a long time appears, it is replaced with a large number. Thus at the end the data is transformed into a sequence of integers; if the data exhibits a lot of local correlations, then these integers tend to be small. Let us give a p ... [...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] |
Lempel–Ziv–Markov Chain Algorithm
The Lempel–Ziv–Markov chain algorithm (LZMA) is an algorithm used to perform 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 statistic .... It has been under development since either 1996 or 1998 by Igor Pavlov (programmer), Igor Pavlov and was first used in the 7z format of the 7-Zip archiver. This algorithm uses a Dictionary coder, 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) - LZMA Unix Port was finally replaced by xz which features better and faster compression; from here we know even LZMA Unix Port was a lot better than gzip and bzip2. and a variable compression-dictionary size (up to 4 Gigabyte, GB), while still ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Binary Tree
In computer science, a binary tree is a k-ary k = 2 tree data structure in which each node has at most two children, which are referred to as the ' and the '. A recursive definition using just set theory notions is that a (non-empty) binary tree is a tuple (''L'', ''S'', ''R''), where ''L'' and ''R'' are binary trees or the empty set and ''S'' is a singleton set containing the root. Some authors allow the binary tree to be the empty set as well. From a graph theory perspective, binary (and K-ary) trees as defined here are arborescences. A binary tree may thus be also called a bifurcating arborescence—a term which appears in some very old programming books, before the modern computer science terminology prevailed. It is also possible to interpret a binary tree as an undirected, rather than a directed graph, in which case a binary tree is an ordered, rooted tree. Some authors use rooted binary tree instead of ''binary tree'' to emphasize the fact that the tree is rooted, bu ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Range Encoding
Range coding (or range encoding) is an entropy coding method defined by G. Nigel N. Martin in a 1979 paper,, 100000)) using the probability distribution . The encoder breaks down the range [0, 100000) into three subranges: A: [ 0, 60000) B: [ 60000, 80000) : [ 80000, 100000) Since our first symbol is an A, it reduces our initial range down to [0, 60000). The second symbol choice leaves us with three sub-ranges of this range. We show them following the already-encoded 'A': AA: [ 0, 36000) AB: [ 36000, 48000) A: [ 48000, 60000) With two symbols encoded, our range is now [0, 36000) and our third symbol leads to the following choices: AAA: [ 0, 21600) AAB: [ 21600, 28800) AA: [ 28800, 36000) This time it is the second of our three choices that represent the message we want to encode, and our range becomes [21600, 28800). It may look harder to determine our sub-ranges in this case, but it is actually not: we can mere ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
