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Audio Data Compression
In signal processing, data compression, source coding,[1] or bit-rate 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
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Source Code
In computing, source code is any collection of computer instructions, possibly with comments, written using[1] a human-readable 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
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Finite-state Machine
A finite-state machine (FSM) or finite-state 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
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Brotli
Brotli
Brotli
is a data format specification[1] for data streams compressed with a specific combination of the general-purpose 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 zlib-compatible implementation of the standard gzip and deflate specifications
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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
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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
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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
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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. 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
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Grammar-based Codes
Grammar-based codes or Grammar-based compression are compression algorithms based on the idea of constructing a context-free 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 grammar-based code transforms x displaystyle x into a context-free grammar G displaystyle G . The problem of finding a smallest grammar for an input sequence is known to be NP-hard,[2] so many grammar-transform algorithms are proposed from theoretical and practical viewpoints
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Sequitur Algorithm
Sequitur (or Nevill-Manning algorithm) is a recursive algorithm developed by Craig Nevill-Manning and Ian H. Witten
Ian H. Witten
in 1997[1] that infers a hierarchical structure (context-free 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
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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 data-generating 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, non-statistical, mathematical models. A statistical model is usually specified by mathematical equations that relate one or more random variables and possibly other non-random variables
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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 not-so-frequently 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 arbitrary-precision fraction q where 0.0 ≤ q < 1.0. It represents the current information as a range, defined by two numbers
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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
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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 24-bit RGB
RGB
color space. It also supports animations and allows a separate palette of up to 256 colors for each frame
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JPEG
JPEG
JPEG
(/ˈdʒeɪpɛɡ/ JAY-peg)[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
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H.263
H.263 is a video compression standard originally designed as a low-bit-rate compressed format for videoconferencing. It was developed by the ITU-T 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 ITU-T, and it was later extended to add various additional enhanced features in 1998 and 2000
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