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98-448: FI is effectively a lossless compression , analogous to gzip , for XML, except that while the original formatting is lost, no information is lost in the conversion from XML to FI, and back to XML. While the purpose of compression is to reduce physical data size, FI aims to optimize both document size and processing performance. The Fast Infoset specification is defined by both the ITU-T and

196-407: A static model, the data is analyzed and a model is constructed, then this model is stored with the compressed data. This approach is simple and modular, but has the disadvantage that the model itself can be expensive to store, and also that it forces using a single model for all data being compressed, and so performs poorly on files that contain heterogeneous data. Adaptive models dynamically update

294-490: A factor of 4 over the Piccolo driver (one of the fastest Java-based XML parsers). Portable devices – Mobile devices typically have low bandwidth data connections and slower CPUs. Fast Infoset uses less bandwidth than XML and is faster to process, making it a superior choice. Storing large volumes of data – When storing XML to either file or database, the volume of data a system produces can often exceed reasonable limits, with

392-494: A form of LPC called adaptive predictive coding (APC), a perceptual coding algorithm that exploited the masking properties of the human ear, followed in the early 1980s with the code-excited linear prediction (CELP) algorithm which achieved a significant compression ratio for its time. Perceptual coding is used by modern audio compression formats such as MP3 and AAC . Discrete cosine transform (DCT), developed by Nasir Ahmed , T. Natarajan and K. R. Rao in 1974, provided

490-415: A further refinement of the direct use of probabilistic modelling , statistical estimates can be coupled to an algorithm called arithmetic coding . Arithmetic coding is a more modern coding technique that uses the mathematical calculations of a finite-state machine to produce a string of encoded bits from a series of input data symbols. It can achieve superior compression compared to other techniques such as

588-450: A higher level with lower resolution continues with the sums. This is called discrete wavelet transform . JPEG2000 additionally uses data points from other pairs and multiplication factors to mix them into the difference. These factors must be integers, so that the result is an integer under all circumstances. So the values are increased, increasing file size, but the distribution of values could be more peaked. The adaptive encoding uses

686-460: A huge versioned document collection, internet archival, etc. The basic task of grammar-based codes is constructing a context-free grammar deriving a single string. Other practical grammar compression algorithms include Sequitur and Re-Pair . The strongest modern lossless compressors use probabilistic models, such as prediction by partial matching . The Burrows–Wheeler transform can also be viewed as an indirect form of statistical modelling. In

784-594: A lossily compressed file for some purpose usually produces a final result inferior to the creation of the same compressed file from an uncompressed original. In addition to sound editing or mixing, lossless audio compression is often used for archival storage, or as master copies. Lossy audio compression is used in a wide range of applications. In addition to standalone audio-only applications of file playback in MP3 players or computers, digitally compressed audio streams are used in most video DVDs, digital television, streaming media on

882-400: A lossy format and a lossless correction; this allows stripping the correction to easily obtain a lossy file. Such formats include MPEG-4 SLS (Scalable to Lossless), WavPack , and OptimFROG DualStream . When audio files are to be processed, either by further compression or for editing , it is desirable to work from an unchanged original (uncompressed or losslessly compressed). Processing of

980-447: A much higher probability than large values. This is often also applied to sound files, and can compress files that contain mostly low frequencies and low volumes. For images, this step can be repeated by taking the difference to the top pixel, and then in videos, the difference to the pixel in the next frame can be taken. A hierarchical version of this technique takes neighboring pairs of data points, stores their difference and sum, and on

1078-435: A number of better-known compression benchmarks. Some benchmarks cover only the data compression ratio , so winners in these benchmarks may be unsuitable for everyday use due to the slow speed of the top performers. Another drawback of some benchmarks is that their data files are known, so some program writers may optimize their programs for best performance on a particular data set. The winners on these benchmarks often come from

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1176-509: A number of detriments: the access times go up as more data is read, CPU load goes up as XML data takes more power to process, and storage costs go up. By storing XML data in Fast Infoset format, data volume may be reduced by as much as 80 percent. Passing XML through the Internet – When an application passes data over the internet, network bandwidth can be a major bottleneck, seriously degrading

1274-532: A particular type of file: for example, lossless audio compression programs do not work well on text files, and vice versa. In particular, files of random data cannot be consistently compressed by any conceivable lossless data compression algorithm; indeed, this result is used to define the concept of randomness in Kolmogorov complexity . It is provably impossible to create an algorithm that can losslessly compress any data. While there have been many claims through

1372-402: A representation of digital data that can be decoded to an exact digital duplicate of the original. Compression ratios are around 50–60% of the original size, which is similar to those for generic lossless data compression. Lossless codecs use curve fitting or linear prediction as a basis for estimating the signal. Parameters describing the estimation and the difference between the estimation and

1470-582: A result, speech can be encoded at high quality using a relatively low bit rate. This is accomplished, in general, by some combination of two approaches: The earliest algorithms used in speech encoding (and audio data compression in general) were the A-law algorithm and the μ-law algorithm . Early audio research was conducted at Bell Labs . There, in 1950, C. Chapin Cutler filed the patent on differential pulse-code modulation (DPCM). In 1973, Adaptive DPCM (ADPCM)

1568-464: A single additional bit is required to tell the decoder that the normal coding has been turned off for the entire input; however, most encoding algorithms use at least one full byte (and typically more than one) for this purpose. For example, deflate compressed files never need to grow by more than 5 bytes per 65,535 bytes of input. In fact, if we consider files of length N, if all files were equally probable, then for any lossless compression that reduces

1666-418: A special case of data differencing . Data differencing consists of producing a difference given a source and a target, with patching reproducing the target given a source and a difference. Since there is no separate source and target in data compression, one can consider data compression as data differencing with empty source data, the compressed file corresponding to a difference from nothing. This

1764-409: A specific type of input data in mind or with specific assumptions about what kinds of redundancy the uncompressed data are likely to contain. Some of the most common lossless compression algorithms are listed below. See list of lossless video codecs Cryptosystems often compress data (the "plaintext") before encryption for added security. When properly implemented, compression greatly increases

1862-408: A table-based compression model where table entries are substituted for repeated strings of data. For most LZ methods, this table is generated dynamically from earlier data in the input. The table itself is often Huffman encoded . Grammar-based codes like this can compress highly repetitive input extremely effectively, for instance, a biological data collection of the same or closely related species,

1960-410: A very small program. However, even though it cannot be determined whether a particular file is incompressible, a simple theorem about incompressible strings shows that over 99% of files of any given length cannot be compressed by more than one byte (including the size of the decompressor). Abstractly, a compression algorithm can be viewed as a function on sequences (normally of octets). Compression

2058-772: A zip file's compressed size includes both the zip file and the unzipping software, since you can not unzip it without both, but there may be an even smaller combined form. Examples of AI-powered audio/video compression software include NVIDIA Maxine , AIVC. Examples of software that can perform AI-powered image compression include OpenCV , TensorFlow , MATLAB 's Image Processing Toolbox (IPT) and High-Fidelity Generative Image Compression. In unsupervised machine learning , k-means clustering can be utilized to compress data by grouping similar data points into clusters. This technique simplifies handling extensive datasets that lack predefined labels and finds widespread use in fields such as image compression . Data compression aims to reduce

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2156-499: 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 signal. Data Compression algorithms present a space-time complexity trade-off between the bytes needed to store or transmit information, and the Computational resources needed to perform

2254-650: Is distinguished as a separate discipline from general-purpose audio compression. Speech coding is used in internet telephony , for example, audio compression is used for CD ripping and is decoded by the audio players. Lossy compression can cause generation loss . The theoretical basis for compression is provided by information theory and, more specifically, Shannon's source coding theorem ; domain-specific theories include algorithmic information theory for lossless compression and rate–distortion theory for lossy compression. These areas of study were essentially created by Claude Shannon , who published fundamental papers on

2352-559: Is important that the original and the decompressed data be identical, or where deviations from the original data would be unfavourable. Common examples are executable programs, text documents, and source code. Some image file formats, like PNG or GIF , use only lossless compression, while others like TIFF and MNG may use either lossless or lossy methods. Lossless audio formats are most often used for archiving or production purposes, while smaller lossy audio files are typically used on portable players and in other cases where storage space

2450-478: Is limited or exact replication of the audio is unnecessary. Most lossless compression programs do two things in sequence: the first step generates a statistical model for the input data, and the second step uses this model to map input data to bit sequences in such a way that "probable" (i.e. frequently encountered) data will produce shorter output than "improbable" data. The primary encoding algorithms used to produce bit sequences are Huffman coding (also used by

2548-416: 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

2646-456: Is made by heuristics ; for example, a compression application may consider files whose names end in ".zip", ".arj" or ".lha" uncompressible without any more sophisticated detection. A common way of handling this situation is quoting input, or uncompressible parts of the input in the output, minimizing the compression overhead. For example, the zip data format specifies the 'compression method' of 'Stored' for input files that have been copied into

2744-436: Is not possible to produce a lossless algorithm that reduces the size of every possible input sequence. Real compression algorithm designers accept that streams of high information entropy cannot be compressed, and accordingly, include facilities for detecting and handling this condition. An obvious way of detection is applying a raw compression algorithm and testing if its output is smaller than its input. Sometimes, detection

2842-411: Is on the order of 23 ms. Speech encoding is an important category of audio data compression. The perceptual models used to estimate what aspects of speech a human ear can hear are generally somewhat different from those used for music. The range of frequencies needed to convey the sounds of a human voice is normally far narrower than that needed for music, and the sound is normally less complex. As

2940-420: Is perceptually irrelevant, most lossy compression algorithms use transforms such as the modified discrete cosine transform (MDCT) to convert time domain sampled waveforms into a transform domain, typically the frequency domain . Once transformed, component frequencies can be prioritized according to how audible they are. Audibility of spectral components is assessed using the absolute threshold of hearing and

3038-426: Is processed. In the minimum case, latency is zero samples (e.g., if the coder/decoder simply reduces the number of bits used to quantize the signal). Time domain algorithms such as LPC also often have low latencies, hence their popularity in speech coding for telephony. In algorithms such as MP3, however, a large number of samples have to be analyzed to implement a psychoacoustic model in the frequency domain, and latency

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3136-429: Is reduced, using methods such as coding , quantization , DCT and linear prediction to reduce the amount of information used to represent the uncompressed data. Lossy audio compression algorithms provide higher compression and are used in numerous audio applications including Vorbis and MP3 . These algorithms almost all rely on psychoacoustics to eliminate or reduce fidelity of less audible sounds, thereby reducing

3234-463: Is sometimes beneficial to compress only the differences between two versions of a file (or, in video compression , of successive images within a sequence). This is called delta encoding (from the Greek letter Δ , which in mathematics, denotes a difference), but the term is typically only used if both versions are meaningful outside compression and decompression. For example, while the process of compressing

3332-436: Is successful if the resulting sequence is shorter than the original sequence (and the instructions for the decompression map). For a compression algorithm to be lossless , the compression map must form an injection from "plain" to "compressed" bit sequences. The pigeonhole principle prohibits a bijection between the collection of sequences of length N and any subset of the collection of sequences of length N −1. Therefore, it

3430-402: Is the same as considering absolute entropy (corresponding to data compression) as a special case of relative entropy (corresponding to data differencing) with no initial data. The term differential compression is used to emphasize the data differencing connection. Entropy coding originated in the 1940s with the introduction of Shannon–Fano coding , the basis for Huffman coding which

3528-435: Is used in digital cameras , to increase storage capacities. Similarly, DVDs , Blu-ray and streaming video use lossy video coding formats . Lossy compression is extensively used in video. In lossy audio compression, methods of psychoacoustics are used to remove non-audible (or less audible) components of the audio signal . Compression of human speech is often performed with even more specialized techniques; speech coding

3626-668: Is used in the GIF format, introduced in 1987. DEFLATE , a lossless compression algorithm specified in 1996, is used in the Portable Network Graphics (PNG) format. Wavelet compression , the use of wavelets in image compression, began after the development of DCT coding. The JPEG 2000 standard was introduced in 2000. In contrast to the DCT algorithm used by the original JPEG format, JPEG 2000 instead uses discrete wavelet transform (DWT) algorithms. JPEG 2000 technology, which includes

3724-582: The ISO / IEC standards bodies. FI is officially defined in ITU-T Rec. X.891 and ISO/IEC 24824-1, and entitled Fast Infoset . The standard was published by ITU-T on May 14, 2005, and by ISO on May 4, 2007. The Fast Infoset standard document can be downloaded from the ITU website . Though the document does not assert intellectual property (IP) restrictions on implementation or use, page ii warns that it has received notices and

3822-625: The Internet , satellite and cable radio, and increasingly in terrestrial radio broadcasts. Lossy compression typically achieves far greater compression than lossless compression, by discarding less-critical data based on psychoacoustic optimizations. Psychoacoustics recognizes that not all data in an audio stream can be perceived by the human auditory system . Most lossy compression reduces redundancy by first identifying perceptually irrelevant sounds, that is, sounds that are very hard to hear. Typical examples include high frequencies or sounds that occur at

3920-473: The LZ77 -based deflate algorithm with a selection of domain-specific prediction filters. However, the patents on LZW expired on June 20, 2003. Many of the lossless compression techniques used for text also work reasonably well for indexed images , but there are other techniques that do not work for typical text that are useful for some images (particularly simple bitmaps), and other techniques that take advantage of

4018-501: The Motion JPEG 2000 extension, was selected as the video coding standard for digital cinema in 2004. Audio data compression, not to be confused with dynamic range compression , has the potential to reduce the transmission bandwidth and storage requirements of audio data. Audio compression formats compression algorithms are implemented in software as audio codecs . In both lossy and lossless compression, information redundancy

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4116-584: The United States and other countries and their legal usage requires licensing by the patent holder. Because of patents on certain kinds of LZW compression, and in particular licensing practices by patent holder Unisys that many developers considered abusive, some open source proponents encouraged people to avoid using the Graphics Interchange Format (GIF) for compressing still image files in favor of Portable Network Graphics (PNG), which combines

4214-470: The University of Buenos Aires . In 1983, using the psychoacoustic principle of the masking of critical bands first published in 1967, he started developing a practical application based on the recently developed IBM PC computer, and the broadcast automation system was launched in 1987 under the name Audicom . 35 years later, almost all the radio stations in the world were using this technology manufactured by

4312-405: The deflate algorithm ) and arithmetic coding . Arithmetic coding achieves compression rates close to the best possible for a particular statistical model, which is given by the information entropy , whereas Huffman compression is simpler and faster but produces poor results for models that deal with symbol probabilities close to 1. There are two primary ways of constructing statistical models: in

4410-500: The discrete cosine transform (DCT). It was first proposed in 1972 by Nasir Ahmed , who then developed a working algorithm with T. Natarajan and K. R. Rao in 1973, before introducing it in January 1974. DCT is the most widely used lossy compression method, and is used in multimedia formats for images (such as JPEG and HEIF ), video (such as MPEG , AVC and HEVC) and audio (such as MP3 , AAC and Vorbis ). Lossy image compression

4508-506: The linear predictive coding (LPC) used with speech, are source-based coders. LPC uses a model of the human vocal tract to analyze speech sounds and infer the parameters used by the model to produce them moment to moment. These changing parameters are transmitted or stored and used to drive another model in the decoder which reproduces the sound. Lossy formats are often used for the distribution of streaming audio or interactive communication (such as in cell phone networks). In such applications,

4606-606: The probability distribution of the input data. An early example of the use of arithmetic coding was in an optional (but not widely used) feature of the JPEG image coding standard. It has since been applied in various other designs including H.263 , H.264/MPEG-4 AVC and HEVC for video coding. Archive software typically has the ability to adjust the "dictionary size", where a larger size demands more random-access memory during compression and decompression, but compresses stronger, especially on repeating patterns in files' content. In

4704-448: The unicity distance by removing patterns that might facilitate cryptanalysis . However, many ordinary lossless compression algorithms produce headers, wrappers, tables, or other predictable output that might instead make cryptanalysis easier. Thus, cryptosystems must utilize compression algorithms whose output does not contain these predictable patterns. Genetics compression algorithms (not to be confused with genetic algorithms ) are

4802-731: The FI specification is available as part of the Eclipse Implementation of JAXB. The library is open source and is distributed under the terms of the Apache License 2.0. Several projects use this implementation, including the reference implementation for JAX-WS used in Eclipse Metro . The QtitanFastInfoset implementation for C++ is available under commercial license as a component for the Qt framework. Because Fast Infosets are compressed as part of

4900-488: The XML Schema, and the XML Schema need not be expressed as an ASN.1 definition. (ASN.1 "Tags" are just type names, e.g. String, Integer, or complex types.) ASN.1 together with ECN is used to define the file format. An index table is built for most strings, which includes element and attribute names, and their values. This means that the text of repeated tags and values only appears once per document. A Java implementation of

4998-474: The XML generation process, they are much faster than using Zip-style compression algorithms on an XML stream, although the output is not as well compressed. SAX-type parsing performance of Fast Infoset is also much faster than parsing performance of XML 1.0, even without any Zip-style compression. Typical increases in parsing speed observed for the reference Java implementation are a factor of 10 over Java Xerces , and

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5096-506: The actual signal are coded separately. A number of lossless audio compression formats exist. See list of lossless codecs for a listing. Some formats are associated with a distinct system, such as Direct Stream Transfer , used in Super Audio CD and Meridian Lossless Packing , used in DVD-Audio , Dolby TrueHD , Blu-ray and HD DVD . Some audio file formats feature a combination of

5194-453: The algorithm, and for any lossless data compression algorithm that makes at least one file smaller, there will be at least one file that it makes larger. This is easily proven with elementary mathematics using a counting argument called the pigeonhole principle , as follows: Most practical compression algorithms provide an "escape" facility that can turn off the normal coding for files that would become longer by being encoded. In theory, only

5292-403: The amount of data required to represent an image at the cost of a relatively small reduction in image quality and has become the most widely used image file format . Its highly efficient DCT-based compression algorithm was largely responsible for the wide proliferation of digital images and digital photos . Lempel–Ziv–Welch (LZW) is a lossless compression algorithm developed in 1984. It

5390-431: The archive verbatim. Mark Nelson, in response to claims of "magic" compression algorithms appearing in comp.compression, has constructed a 415,241 byte binary file of highly entropic content, and issued a public challenge of $ 100 to anyone to write a program that, together with its input, would be smaller than his provided binary data yet be able to reconstitute it without error. A similar challenge, with $ 5,000 as reward,

5488-418: The basis for the modified discrete cosine transform (MDCT) used by modern audio compression formats such as MP3, Dolby Digital , and AAC. MDCT was proposed by J. P. Princen, A. W. Johnson and A. B. Bradley in 1987, following earlier work by Princen and Bradley in 1986. The world's first commercial broadcast automation audio compression system was developed by Oscar Bonello, an engineering professor at

5586-487: The better-known Huffman algorithm. It uses an internal memory state to avoid the need to perform a one-to-one mapping of individual input symbols to distinct representations that use an integer number of bits, and it clears out the internal memory only after encoding the entire string of data symbols. Arithmetic coding applies especially well to adaptive data compression tasks where the statistics vary and are context-dependent, as it can be easily coupled with an adaptive model of

5684-580: The class of context-mixing compression software. Matt Mahoney , in his February 2010 edition of the free booklet Data Compression Explained , additionally lists the following: The Compression Ratings website published a chart summary of the "frontier" in compression ratio and time. The Compression Analysis Tool is a Windows application that enables end users to benchmark the performance characteristics of streaming implementations of LZF4, Deflate, ZLIB, GZIP, BZIP2 and LZMA using their own data. It produces measurements and charts with which users can compare

5782-410: The coding algorithm can be critical; for example, when there is a two-way transmission of data, such as with a telephone conversation, significant delays may seriously degrade the perceived quality. In contrast to the speed of compression, which is proportional to the number of operations required by the algorithm, here latency refers to the number of samples that must be analyzed before a block of audio

5880-439: 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

5978-429: The compression speed, decompression speed and compression ratio of the different compression methods and to examine how the compression level, buffer size and flushing operations affect the results. Lossless data compression algorithms cannot guarantee compression for all input data sets. In other words, for any lossless data compression algorithm, there will be an input data set that does not get smaller when processed by

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6076-654: The core information of the original data while significantly decreasing the required storage space. Large language models (LLMs) are also capable of lossless data compression, as demonstrated by DeepMind 's research with the Chinchilla 70B model. Developed by DeepMind, Chinchilla 70B effectively compressed data, outperforming conventional methods such as Portable Network Graphics (PNG) for images and Free Lossless Audio Codec (FLAC) for audio. It achieved compression of image and audio data to 43.4% and 16.4% of their original sizes, respectively. Data compression can be viewed as

6174-467: The data in question. For example, the human eye is more sensitive to subtle variations in luminance than it is to the variations in color. JPEG image compression works in part by rounding off nonessential bits of information. A number of popular compression formats exploit these perceptual differences, including psychoacoustics for sound, and psychovisuals for images and video. Most forms of lossy compression are based on transform coding , especially

6272-462: The data must be decompressed as the data flows, rather than after the entire data stream has been transmitted. Not all audio codecs can be used for streaming applications. Latency is introduced by the methods used to encode and decode the data. Some codecs will analyze a longer segment, called a frame , of the data to optimize efficiency, and then code it in a manner that requires a larger segment of data at one time to decode. The inherent latency of

6370-417: The encoding and decoding. The design of data compression schemes involves balancing the degree of compression, the amount of distortion introduced (when using lossy data compression ), and the computational resources or time required to compress and decompress the data. Lossless data compression algorithms usually exploit statistical redundancy to represent data without losing any information , so that

6468-434: The end of a list of child-elements. Binary data is transmitted in native format, and need not be converted to a transmission format such as base64 . Fast Infoset is a higher level format built on ASN.1 forms and notation. Element and attribute names are stored within the octet stream, unlike traditional ASN.1 encoding schemes. In consequence, The conventional XML file can be recovered from the binary stream without reference

6566-455: The error in the above-mentioned lossless audio compression scheme could be described as delta encoding from the approximated sound wave to the original sound wave, the approximated version of the sound wave is not meaningful in any other context. No lossless compression algorithm can efficiently compress all possible data (see § Limitations for more on this) . For this reason, many different algorithms exist that are designed either with

6664-435: The fact that DNA sequences have characteristic properties, such as inverted repeats. The most successful compressors are XM and GeCo. For eukaryotes XM is slightly better in compression ratio, though for sequences larger than 100 MB its computational requirements are impractical. Self-extracting executables contain a compressed application and a decompressor. When executed, the decompressor transparently decompresses and runs

6762-469: The file size is reduced to 5-20% of the original size and a megabyte can store about a minute's worth of music at adequate quality. Several proprietary lossy compression algorithms have been developed that provide higher quality audio performance by using a combination of lossless and lossy algorithms with adaptive bit rates and lower compression ratios. Examples include aptX , LDAC , LHDC , MQA and SCL6 . To determine what information in an audio signal

6860-416: The form for which they were designed to compress. Many of the lossless compression techniques used for text also work reasonably well for indexed images . These techniques take advantage of the specific characteristics of images such as the common phenomenon of contiguous 2-D areas of similar tones. Every pixel but the first is replaced by the difference to its left neighbor. This leads to small values having

6958-455: The late 1980s, digital images became more common, and standards for lossless image compression emerged. In the early 1990s, lossy compression methods began to be widely used. In these schemes, some loss of information is accepted as dropping nonessential detail can save storage space. There is a corresponding trade-off between preserving information and reducing size. Lossy data compression schemes are designed by research on how people perceive

7056-679: The latest generation of lossless algorithms that compress data (typically sequences of nucleotides) using both conventional compression algorithms and specific algorithms adapted to genetic data. In 2012, a team of scientists from Johns Hopkins University published the first genetic compression algorithm that does not rely on external genetic databases for compression. HAPZIPPER was tailored for HapMap data and achieves over 20-fold compression (95% reduction in file size), providing 2- to 4-fold better compression much faster than leading general-purpose compression utilities. Genomic sequence compression algorithms, also known as DNA sequence compressors, explore

7154-469: The main lesson from the argument is not that one risks big losses, but merely that one cannot always win. To choose an algorithm always means implicitly to select a subset of all files that will become usefully shorter. This is the theoretical reason why we need to have different compression algorithms for different kinds of files: there cannot be any algorithm that is good for all kinds of data. The "trick" that allows lossless compression algorithms, used on

7252-627: The model as the data is compressed. Both the encoder and decoder begin with a trivial model, yielding poor compression of initial data, but as they learn more about the data, performance improves. Most popular types of compression used in practice now use adaptive coders. Lossless compression methods may be categorized according to the type of data they are designed to compress. While, in principle, any general-purpose lossless compression algorithm ( general-purpose meaning that they can accept any bitstring) can be used on any type of data, many are unable to achieve significant compression on data that are not of

7350-507: The most popular algorithms for lossless storage. DEFLATE is a variation on LZ optimized for decompression speed and compression ratio, but compression can be slow. In the mid-1980s, following work by Terry Welch , the Lempel–Ziv–Welch (LZW) algorithm rapidly became the method of choice for most general-purpose compression systems. LZW is used in GIF images, programs such as PKZIP , and hardware devices such as modems. LZ methods use

7448-413: The original application. This is especially often used in demo coding, where competitions are held for demos with strict size limits, as small as 1 kilobyte . This type of compression is not strictly limited to binary executables, but can also be applied to scripts, such as JavaScript . Lossless compression algorithms and their implementations are routinely tested in head-to-head benchmarks . There are

7546-410: The other hand, it has also been proven that there is no algorithm to determine whether a file is incompressible in the sense of Kolmogorov complexity. Hence it is possible that any particular file, even if it appears random, may be significantly compressed, even including the size of the decompressor. An example is the digits of the mathematical constant pi , which appear random but can be generated by

7644-436: The performance of client applications and limiting the server's power to process requests. Reducing the size of data transferred across the internet reduces the time required to send or receive the message, and increases the number of transactions a server can process per hour. Lossless compression Lossless compression is a class of data compression that allows the original data to be perfectly reconstructed from

7742-473: The principles of simultaneous masking —the phenomenon wherein a signal is masked by another signal separated by frequency—and, in some cases, temporal masking —where a signal is masked by another signal separated by time. Equal-loudness contours may also be used to weigh the perceptual importance of components. Models of the human ear-brain combination incorporating such effects are often called psychoacoustic models . Other types of lossy compressors, such as

7840-404: The probabilities from the previous sample in sound encoding, from the left and upper pixel in image encoding, and additionally from the previous frame in video encoding. In the wavelet transformation, the probabilities are also passed through the hierarchy. Many of these methods are implemented in open-source and proprietary tools, particularly LZW and its variants. Some algorithms are patented in

7938-472: The process is reversible. Lossless compression is possible because most real-world data exhibits statistical redundancy. For example, an image may have areas of color that do not change over several pixels; instead of coding "red pixel, red pixel, ..." the data may be encoded as "279 red pixels". This is a basic example of run-length encoding ; there are many schemes to reduce file size by eliminating redundancy. The Lempel–Ziv (LZ) compression methods are among

8036-488: The same time as louder sounds. Those irrelevant sounds are coded with decreased accuracy or not at all. Due to the nature of lossy algorithms, audio quality suffers a digital generation loss when a file is decompressed and recompressed. This makes lossy compression unsuitable for storing the intermediate results in professional audio engineering applications, such as sound editing and multitrack recording. However, lossy formats such as MP3 are very popular with end-users as

8134-405: 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

8232-546: The size of data files, enhancing storage efficiency and speeding up data transmission. K-means clustering, an unsupervised machine learning algorithm, is employed to partition a dataset into a specified number of clusters, k, each represented by the centroid of its points. This process condenses extensive datasets into a more compact set of representative points. Particularly beneficial in image and signal processing , k-means clustering aids in data reduction by replacing groups of data points with their centroids, thereby preserving

8330-470: The size of some file, the expected length of a compressed file (averaged over all possible files of length N) must necessarily be greater than N. So if we know nothing about the properties of the data we are compressing, we might as well not compress it at all. A lossless compression algorithm is useful only when we are more likely to compress certain types of files than others; then the algorithm could be designed to compress those types of data better. Thus,

8428-592: The space required to store or transmit them. The acceptable trade-off between loss of audio quality and transmission or storage size depends upon the application. For example, one 640 MB compact disc (CD) holds approximately one hour of uncompressed high fidelity music, less than 2 hours of music compressed losslessly, or 7 hours of music compressed in the MP3 format at a medium bit rate . A digital sound recorder can typically store around 200 hours of clearly intelligible speech in 640 MB. Lossless audio compression produces

8526-426: The specific characteristics of images (such as the common phenomenon of contiguous 2-D areas of similar tones, and the fact that color images usually have a preponderance of a limited range of colors out of those representable in the color space). As mentioned previously, lossless sound compression is a somewhat specialized area. Lossless sound compression algorithms can take advantage of the repeating patterns shown by

8624-624: The subject may not be completely free of IP assertions. A common misconception is that FI requires ASN.1 tool support. Although the formal specification uses ASN.1 notation, the standard includes Encoding Control Notation (ECN) and ASN.1 tools are not required by implementations. An alternative to FI is FleXPath. The underlying file format is ASN.1 , with tag/length/value blocks. Text values of attributes and elements are stored with length prefixes rather than end delimiters, and data segments do not require escapement for special characters. The equivalent of end tags ("terminators") are needed only at

8722-511: The symbol that compresses best, given the previous history). This equivalence has been used as a justification for using data compression as a benchmark for "general intelligence". An alternative view can show compression algorithms implicitly map strings into implicit feature space vectors , and compression-based similarity measures compute similarity within these feature spaces. For each compressor C(.) we define an associated vector space ℵ, such that C(.) maps an input string x, corresponding to

8820-478: The topic in the late 1940s and early 1950s. Other topics associated with compression include coding theory and statistical inference . There is a close connection between machine learning and compression. A system that predicts the posterior probabilities of a sequence given its entire history can be used for optimal data compression (by using arithmetic coding on the output distribution). Conversely, an optimal compressor can be used for prediction (by finding

8918-437: The type of data they were designed for, to consistently compress such files to a shorter form is that the files the algorithms are designed to act on all have some form of easily modeled redundancy that the algorithm is designed to remove, and thus belong to the subset of files that that algorithm can make shorter, whereas other files would not get compressed or even get bigger. Algorithms are generally quite specifically tuned to

9016-505: The uncompressed data are likely to contain. Lossless data compression is used in many applications. For example, it is used in the ZIP file format and in the GNU tool gzip . It is also often used as a component within lossy data compression technologies (e.g. lossless mid/side joint stereo preprocessing by MP3 encoders and other lossy audio encoders). Lossless compression is used in cases where it

9114-503: The vector norm ||~x||. An exhaustive examination of the feature spaces underlying all compression algorithms is precluded by space; instead, feature vectors chooses to examine three representative lossless compression methods, LZW, LZ77, and PPM. According to AIXI theory, a connection more directly explained in Hutter Prize , the best possible compression of x is the smallest possible software that generates x. For example, in that model,

9212-472: The wave-like nature of the data ‍ — ‍ essentially using autoregressive models to predict the "next" value and encoding the (possibly small) difference between the expected value and the actual data. If the difference between the predicted and the actual data (called the error ) tends to be small, then certain difference values (like 0, +1, −1 etc. on sample values) become very frequent, which can be exploited by encoding them in few output bits. It

9310-452: The years of companies achieving "perfect compression" where an arbitrary number N of random bits can always be compressed to N  − 1 bits, these kinds of claims can be safely discarded without even looking at any further details regarding the purported compression scheme. Such an algorithm contradicts fundamental laws of mathematics because, if it existed, it could be applied repeatedly to losslessly reduce any file to length 1. On

9408-591: Was developed in 1950. Transform coding dates back to the late 1960s, with the introduction of fast Fourier transform (FFT) coding in 1968 and the Hadamard transform in 1969. An important image compression technique is the discrete cosine transform (DCT), a technique developed in the early 1970s. DCT is the basis for JPEG, a lossy compression format which was introduced by the Joint Photographic Experts Group (JPEG) in 1992. JPEG greatly reduces

9506-425: Was introduced by P. Cummiskey, Nikil S. Jayant and James L. Flanagan . Perceptual coding was first used for speech coding compression, with linear predictive coding (LPC). Initial concepts for LPC date back to the work of Fumitada Itakura ( Nagoya University ) and Shuzo Saito ( Nippon Telegraph and Telephone ) in 1966. During the 1970s, Bishnu S. Atal and Manfred R. Schroeder at Bell Labs developed

9604-407: Was issued by Mike Goldman. 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

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