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Audio Data Compression In signal processing, data compression, source coding,[1] or bitrate reduction involves encoding information using fewer bits than the original representation.[2] Compression can be 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.[3] 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.[4] 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. Compression is useful because it reduces resources required to store and transmit data [...More...]  "Audio Data Compression" on: Wikipedia Yahoo 

Source Code In computing, source code is any collection of computer instructions, possibly with comments, written using[1] a humanreadable programming language, usually as plain text. The source code of a program is specially designed to facilitate the work of computer programmers, who specify the actions to be performed by a computer mostly by writing source code. The source code is often transformed by an assembler or compiler into binary machine code understood by the computer. The machine code might then be stored for execution at a later time. Alternatively, source code may be interpreted and thus immediately executed. Most application software is distributed in a form that includes only executable files [...More...]  "Source Code" on: Wikipedia Yahoo 

Finitestate Machine A finitestate machine (FSM) or finitestate automaton (FSA, plural: automata), finite automaton, or simply a state machine, is a mathematical model of computation. It is an abstract machine that can be in exactly one of a finite number of states at any given time. The FSM can change from one state to another in response to some external inputs; the change from one state to another is called a transition. An FSM is defined by a list of its states, its initial state, and the conditions for each transition [...More...]  "Finitestate Machine" on: Wikipedia Yahoo 

Brotli Brotli Brotli is a data format specification[1] for data streams compressed with a specific combination of the generalpurpose LZ77 lossless compression algorithm, Huffman coding Huffman coding and 2nd order context modelling. Brotli Brotli was initially developed to decrease the size of transmissions of WOFF2 web fonts, and in that context was a continuation of the development of zopfli, which is a zlibcompatible implementation of the standard gzip and deflate specifications [...More...]  "Brotli" on: Wikipedia Yahoo 

LZX (algorithm) LZX is an LZ77 family compression algorithm. It is also the name of a file archiver with the same name. Both were invented by Jonathan Forbes and Tomi Poutanen in 1990s.Contents1 Instances of use of the LZX algorithm1.1 Amiga Amiga LZX 1.2 Microsoft Microsoft Cabinet files 1.3 Microsoft Microsoft Compressed HTML Help (CHM) files 1.4 Microsoft Microsoft Reader (LIT) files 1.5 Windows Imaging Format (WIM) files 1.6 Xbox Live Avatars2 Decompressing LZX files 3 See also 4 References 5 External linksInstances of use of the LZX algorithm[edit] Amiga Amiga LZX[edit] LZX was publicly released as an Amiga Amiga file archiver in 1995, while the authors were studying at the University of Waterloo University of Waterloo in Canada [...More...]  "LZX (algorithm)" on: Wikipedia Yahoo 

Probabilistic Algorithm A randomized algorithm is an algorithm that employs a degree of randomness as part of its logic. The algorithm typically uses uniformly random bits as an auxiliary input to guide its behavior, in the hope of achieving good performance in the "average case" over all possible choices of random bits [...More...]  "Probabilistic Algorithm" on: Wikipedia Yahoo 

Prediction By Partial Matching Prediction 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.Contents1 Theory 2 Implementation 3 See also 4 Sources 5 References 6 External linksTheory[edit] Predictions are usually reduced to symbol rankings[clarification needed]. 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 [...More...]  "Prediction By Partial Matching" on: Wikipedia Yahoo 

Burrows–Wheeler Transform The Burrows–Wheeler transform (BWT, also called blocksorting 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 movetofront transform and runlength encoding. More importantly, the transformation is reversible, without needing to store any additional data. The BWT is thus a "free" method of improving the efficiency of text compression algorithms, costing only some extra computation.Contents1 Description 2 Example 3 Explanation 4 Optimization 5 Bijective Bijective variant 6 Dynamic Burrows–Wheeler transform 7 Sample implementation 8 BWT in bioinformatics 9 References 10 External linksDescription[edit] The Burrows–Wheeler transform is an algorithm used to prepare data for use with data compression techniques such as bzip2 [...More...]  "Burrows–Wheeler Transform" on: Wikipedia Yahoo 

Grammarbased Codes Grammarbased codes or Grammarbased compression are compression algorithms based on the idea of constructing a contextfree grammar (CFG) for the string to be compressed. Examples include universal lossless data compression algorithms.[1] To compress a data sequence x = x 1 ⋯ x n displaystyle x=x_ 1 cdots x_ n , a grammarbased code transforms x displaystyle x into a contextfree grammar G displaystyle G . The problem of finding a smallest grammar for an input sequence is known to be NPhard,[2] so many grammartransform algorithms are proposed from theoretical and practical viewpoints [...More...]  "Grammarbased Codes" on: Wikipedia Yahoo 

Sequitur Algorithm Sequitur (or NevillManning algorithm) is a recursive algorithm developed by Craig NevillManning and Ian H. Witten Ian H. Witten in 1997[1] that infers a hierarchical structure (contextfree grammar) from a sequence of discrete symbols. The algorithm operates in linear space and time. It can be used in data compression software applications.[2]Contents1 Constraints1.1 Digram uniqueness 1.2 Rule utility2 Method summary 3 See also 4 References 5 External linksConstraints[edit] The sequitur algorithm constructs a grammar by substituting repeating phrases in the given sequence with new rules and therefore produces a concise representation of the sequence [...More...]  "Sequitur Algorithm" on: Wikipedia Yahoo 

Probabilistic Model A statistical model is a mathematical model that embodies a set of statistical assumptions concerning the generation of some sample data and similar data from a larger population. A statistical model represents, often in considerably idealized form, the datagenerating process. The assumptions embodied by a statistical model describe a set of probability distributions, some of which are assumed to adequately approximate the distribution from which a particular data set is sampled. The probability distributions inherent in statistical models are what distinguishes statistical models from other, nonstatistical, mathematical models. A statistical model is usually specified by mathematical equations that relate one or more random variables and possibly other nonrandom variables [...More...]  "Probabilistic Model" on: Wikipedia Yahoo 

Arithmetic Coding Arithmetic coding Arithmetic coding is a form of entropy encoding used in lossless data compression. Normally, a string of characters such as the words "hello there" 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 notsofrequently occurring characters will be stored with more bits, resulting in fewer bits used in total. Arithmetic coding 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 arbitraryprecision fraction q where 0.0 ≤ q < 1.0. It represents the current information as a range, defined by two numbers [...More...]  "Arithmetic Coding" on: Wikipedia Yahoo 

Probability Distribution In probability theory and statistics, a probability distribution is a mathematical function that, stated in simple terms, can be thought of as providing the probabilities of occurrence of different possible outcomes in an experiment. For instance, if the random variable X is used to denote the outcome of a coin toss ("the experiment"), then the probability distribution of X would take the value 0.5 for X = heads, and 0.5 for X = tails (assuming the coin is fair). In more technical terms, the probability distribution is a description of a random phenomenon in terms of the probabilities of events. Examples of random phenomena can include the results of an experiment or survey. A probability distribution is defined in terms of an underlying sample space, which is the set of all possible outcomes of the random phenomenon being observed [...More...]  "Probability Distribution" on: Wikipedia Yahoo 

Graphics Interchange Format The Graphics Interchange Format (better known by its acronym GIF (/ɡɪf/ GHIF or /dʒɪf/ JIF)) is a bitmap image format that was developed by a team at the bulletin board service (BBS) provider CompuServe CompuServe led by American computer scientist Steve Wilhite on June 15, 1987.[1] It has since come into widespread usage on the World Wide Web due to its wide support and portability. The format supports up to 8 bits per pixel for each image, allowing a single image to reference its own palette of up to 256 different colors chosen from the 24bit RGB RGB color space. It also supports animations and allows a separate palette of up to 256 colors for each frame [...More...]  "Graphics Interchange Format" on: Wikipedia Yahoo 

JPEG JPEG JPEG (/ˈdʒeɪpɛɡ/ JAYpeg)[1] is a commonly used method of lossy compression for digital images, particularly for those images produced by digital photography. The degree of compression can be adjusted, allowing a selectable tradeoff between storage size and image quality. JPEG JPEG typically achieves 10:1 compression with little perceptible loss in image quality.[2] JPEG JPEG compression is used in a number of image file formats. JPEG/Exif is the most common image format used by digital cameras and other photographic image capture devices; along with JPEG/JFIF, it is the most common format for storing and transmitting photographic images on the World Wide Web.[3] These format variations are often not distinguished, and are simply called JPEG. The term "JPEG" is an initialism/acronym for the Joint Photographic Experts Group, which created the standard [...More...]  "JPEG" on: Wikipedia Yahoo 

H.263 H.263 is a video compression standard originally designed as a lowbitrate compressed format for videoconferencing. It was developed by the ITUT Video Coding Experts Group (VCEG) in a project ending in 1995/1996 as one member of the H.26x family of video coding standards in the domain of the ITUT, and it was later extended to add various additional enhanced features in 1998 and 2000 [...More...]  "H.263" on: Wikipedia Yahoo 