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Wikipedia

Data compression

In information theory, data compression, source coding,[1] or bit-rate reduction is the process of encoding information using fewer bits than the original representation.[2] 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.[3] 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.[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 the resources required to store and transmit data. Computational resources are consumed in the compression and decompression processes. Data compression is subject to a space-time complexity trade-off. For instance, a compression scheme for video may require expensive hardware for the video to be decompressed fast enough to be viewed as it is being decompressed, and the option to decompress the video in full before watching it may be inconvenient or require additional storage. The design of data compression schemes involves trade-offs among various factors, including the degree of compression, the amount of distortion introduced (when using lossy data compression), and the computational resources required to compress and decompress the data.[5]

Lossless Edit

Lossless data compression algorithms usually exploit statistical redundancy to represent data without losing any information, so that 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 the most popular algorithms for lossless storage.[6] 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.[7] LZ methods use 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, 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.[8] In 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 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 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.[9] It has since been applied in various other designs including H.263, H.264/MPEG-4 AVC and HEVC for video coding.[10]

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.[11][12]

Lossy Edit

 
MP3, an example of a lossy file format compared to WAV.

In 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.[13] 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 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.[14] 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 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.[15][16] DCT is the most widely used lossy compression method, and is used in multimedia formats for images (such as JPEG and HEIF),[17] video (such as MPEG, AVC and HEVC) and audio (such as MP3, AAC and Vorbis).

Lossy image compression 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 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.[8]

Lossy compression can cause generation loss.

Theory Edit

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 the topic in the late 1940s and early 1950s. Other topics associated with compression include coding theory and statistical inference.[18]

Machine learning Edit

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 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".[19][20][21]

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 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.[22]

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, 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 VP9, NVIDIA Maxine, AIVC, AccMPEG.[23] 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.[24]

Data differencing Edit

Data compression can be viewed as a special case of data differencing.[25][26] 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 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.

Uses Edit

Image Edit

Entropy coding originated in the 1940s with the introduction of Shannon–Fano coding,[27] the basis for Huffman coding which was developed in 1950.[28] 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.[29]

An important image compression technique is the discrete cosine transform (DCT), a technique developed in the early 1970s.[15] DCT is the basis for JPEG, a lossy compression format which was introduced by the Joint Photographic Experts Group (JPEG) in 1992.[30] JPEG greatly reduces 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.[31][32] Its highly efficient DCT-based compression algorithm was largely responsible for the wide proliferation of digital images and digital photos.[33]

Lempel–Ziv–Welch (LZW) is a lossless compression algorithm developed in 1984. It is used in the GIF format, introduced in 1987.[34] DEFLATE, a lossless compression algorithm specified in 1996, is used in the Portable Network Graphics (PNG) format.[35]

Wavelet compression, the use of wavelets in image compression, began after the development of DCT coding.[36] The JPEG 2000 standard was introduced in 2000.[37] In contrast to the DCT algorithm used by the original JPEG format, JPEG 2000 instead uses discrete wavelet transform (DWT) algorithms.[38][39][40] JPEG 2000 technology, which includes the Motion JPEG 2000 extension, was selected as the video coding standard for digital cinema in 2004.[41]

Audio Edit

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 algorithms are implemented in software as audio codecs. In both lossy and lossless compression, information redundancy 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 the space required to store or transmit them.[2][42]

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.[43]

Lossless audio compression produces 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,[44] 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 the actual signal are coded separately.[45]

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 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 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 Edit

 
Comparison of spectrograms of audio in an uncompressed format and several lossy formats. The lossy spectrograms show bandlimiting of higher frequencies, a common technique associated with lossy audio compression.

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 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.[46]

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 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 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.

Coding methods Edit

To determine what information in an audio signal 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 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.[47]

Other types of lossy compressors, such as 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, 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.[46]

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 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 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 is on the order of 23 ms.

Speech encoding Edit

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 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:

  • Only encoding sounds that could be made by a single human voice.
  • Throwing away more of the data in the signal—keeping just enough to reconstruct an "intelligible" voice rather than the full frequency range of human hearing.

The earliest algorithms used in speech encoding (and audio data compression in general) were the A-law algorithm and the μ-law algorithm.

History Edit

 
Solidyne 922: The world's first commercial audio bit compression sound card for PC, 1990

Early audio research was conducted at Bell Labs. There, in 1950, C. Chapin Cutler filed the patent on differential pulse-code modulation (DPCM).[48] In 1973, Adaptive DPCM (ADPCM) was introduced by P. Cummiskey, Nikil S. Jayant and James L. Flanagan.[49][50]

Perceptual coding was first used for speech coding compression, with linear predictive coding (LPC).[51] Initial concepts for LPC date back to the work of Fumitada Itakura (Nagoya University) and Shuzo Saito (Nippon Telegraph and Telephone) in 1966.[52] During the 1970s, Bishnu S. Atal and Manfred R. Schroeder at Bell Labs developed 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.[51] Perceptual coding is used by modern audio compression formats such as MP3[51] and AAC.

Discrete cosine transform (DCT), developed by Nasir Ahmed, T. Natarajan and K. R. Rao in 1974,[16] provided the basis for the modified discrete cosine transform (MDCT) used by modern audio compression formats such as MP3,[53] Dolby Digital,[54][55] and AAC.[56] MDCT was proposed by J. P. Princen, A. W. Johnson and A. B. Bradley in 1987,[57] following earlier work by Princen and Bradley in 1986.[58]

The world's first commercial broadcast automation audio compression system was developed by Oscar Bonello, an engineering professor at the University of Buenos Aires. [59] In 1983, using the psychoacoustic principle of the masking of critical bands first published in 1967,[60] 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. [61] 35 years later, almost all the radio stations in the world were using this technology manufactured by a number of companies because the inventor refuses to get invention patents for his work. He prefers declaring it of Public Domain publishing it [62]

A literature compendium for a large variety of audio coding systems was published in the IEEE's Journal on Selected Areas in Communications (JSAC), in February 1988. While there were some papers from before that time, this collection documented an entire variety of finished, working audio coders, nearly all of them using perceptual techniques and some kind of frequency analysis and back-end noiseless coding.[63]

Video Edit

Uncompressed video requires a very high data rate. Although lossless video compression codecs perform at a compression factor of 5 to 12, a typical H.264 lossy compression video has a compression factor between 20 and 200.[64]

The two key video compression techniques used in video coding standards are the DCT and motion compensation (MC). Most video coding standards, such as the H.26x and MPEG formats, typically use motion-compensated DCT video coding (block motion compensation).[65][66]

Most video codecs are used alongside audio compression techniques to store the separate but complementary data streams as one combined package using so-called container formats.[67]

Encoding theory Edit

Video data may be represented as a series of still image frames. Such data usually contains abundant amounts of spatial and temporal redundancy. Video compression algorithms attempt to reduce redundancy and store information more compactly.

Most video compression formats and codecs exploit both spatial and temporal redundancy (e.g. through difference coding with motion compensation). Similarities can be encoded by only storing differences between e.g. temporally adjacent frames (inter-frame coding) or spatially adjacent pixels (intra-frame coding). Inter-frame compression (a temporal delta encoding) (re)uses data from one or more earlier or later frames in a sequence to describe the current frame. Intra-frame coding, on the other hand, uses only data from within the current frame, effectively being still-image compression.[47]

The intra-frame video coding formats used in camcorders and video editing employ simpler compression that uses only intra-frame prediction. This simplifies video editing software, as it prevents a situation in which a compressed frame refers to data that the editor has deleted.

Usually, video compression additionally employs lossy compression techniques like quantization that reduce aspects of the source data that are (more or less) irrelevant to the human visual perception by exploiting perceptual features of human vision. For example, small differences in color are more difficult to perceive than are changes in brightness. Compression algorithms can average a color across these similar areas in a manner similar to those used in JPEG image compression.[9] As in all lossy compression, there is a trade-off between video quality and bit rate, cost of processing the compression and decompression, and system requirements. Highly compressed video may present visible or distracting artifacts.

Other methods other than the prevalent DCT-based transform formats, such as fractal compression, matching pursuit and the use of a discrete wavelet transform (DWT), have been the subject of some research, but are typically not used in practical products. Wavelet compression is used in still-image coders and video coders without motion compensation. Interest in fractal compression seems to be waning, due to recent theoretical analysis showing a comparative lack of effectiveness of such methods.[47]

Inter-frame coding Edit

In inter-frame coding, individual frames of a video sequence are compared from one frame to the next, and the video compression codec records the differences to the reference frame. If the frame contains areas where nothing has moved, the system can simply issue a short command that copies that part of the previous frame into the next one. If sections of the frame move in a simple manner, the compressor can emit a (slightly longer) command that tells the decompressor to shift, rotate, lighten, or darken the copy. This longer command still remains much shorter than data generated by intra-frame compression. Usually, the encoder will also transmit a residue signal which describes the remaining more subtle differences to the reference imagery. Using entropy coding, these residue signals have a more compact representation than the full signal. In areas of video with more motion, the compression must encode more data to keep up with the larger number of pixels that are changing. Commonly during explosions, flames, flocks of animals, and in some panning shots, the high-frequency detail leads to quality decreases or to increases in the variable bitrate.

Hybrid block-based transform formats Edit

 
Processing stages of a typical video encoder

Today,[as of?] nearly all commonly used video compression methods (e.g., those in standards approved by the ITU-T or ISO) share the same basic architecture that dates back to H.261 which was standardized in 1988 by the ITU-T. They mostly rely on the DCT, applied to rectangular blocks of neighboring pixels, and temporal prediction using motion vectors, as well as nowadays also an in-loop filtering step.

In the prediction stage, various deduplication and difference-coding techniques are applied that help decorrelate data and describe new data based on already transmitted data.

Then rectangular blocks of remaining pixel data are transformed to the frequency domain. In the main lossy processing stage, frequency domain data gets quantized in order to reduce information that is irrelevant to human visual perception.

In the last stage statistical redundancy gets largely eliminated by an entropy coder which often applies some form of arithmetic coding.

In an additional in-loop filtering stage various filters can be applied to the reconstructed image signal. By computing these filters also inside the encoding loop they can help compression because they can be applied to reference material before it gets used in the prediction process and they can be guided using the original signal. The most popular example are deblocking filters that blur out blocking artifacts from quantization discontinuities at transform block boundaries.

History Edit

In 1967, A.H. Robinson and C. Cherry proposed a run-length encoding bandwidth compression scheme for the transmission of analog television signals.[68] The DCT, which is fundamental to modern video compression,[69] was introduced by Nasir Ahmed, T. Natarajan and K. R. Rao in 1974.[16][70]

H.261, which debuted in 1988, commercially introduced the prevalent basic architecture of video compression technology.[71] It was the first video coding format based on DCT compression.[69] H.261 was developed by a number of companies, including Hitachi, PictureTel, NTT, BT and Toshiba.[72]

The most popular video coding standards used for codecs have been the MPEG standards. MPEG-1 was developed by the Motion Picture Experts Group (MPEG) in 1991, and it was designed to compress VHS-quality video. It was succeeded in 1994 by MPEG-2/H.262,[71] which was developed by a number of companies, primarily Sony, Thomson and Mitsubishi Electric.[73] MPEG-2 became the standard video format for DVD and SD digital television.[71] In 1999, it was followed by MPEG-4/H.263.[71] It was also developed by a number of companies, primarily Mitsubishi Electric, Hitachi and Panasonic.[74]

H.264/MPEG-4 AVC was developed in 2003 by a number of organizations, primarily Panasonic, Godo Kaisha IP Bridge and LG Electronics.[75] AVC commercially introduced the modern context-adaptive binary arithmetic coding (CABAC) and context-adaptive variable-length coding (CAVLC) algorithms. AVC is the main video encoding standard for Blu-ray Discs, and is widely used by video sharing websites and streaming internet services such as YouTube, Netflix, Vimeo, and iTunes Store, web software such as Adobe Flash Player and Microsoft Silverlight, and various HDTV broadcasts over terrestrial and satellite television.

Genetics Edit

Genetics compression algorithms are the latest generation of lossless algorithms that compress data (typically sequences of nucleotides) using both conventional compression algorithms and genetic algorithms adapted to the specific datatype. In 2012, a team of scientists from Johns Hopkins University published a genetic compression algorithm that does not use a reference genome 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 and is less computationally intensive than the leading general-purpose compression utilities. For this, Chanda, Elhaik, and Bader introduced MAF-based encoding (MAFE), which reduces the heterogeneity of the dataset by sorting SNPs by their minor allele frequency, thus homogenizing the dataset.[76] Other algorithms developed in 2009 and 2013 (DNAZip and GenomeZip) have compression ratios of up to 1200-fold—allowing 6 billion basepair diploid human genomes to be stored in 2.5 megabytes (relative to a reference genome or averaged over many genomes).[77][78] For a benchmark in genetics/genomics data compressors, see [79]

Outlook and currently unused potential Edit

It is estimated that the total amount of data that is stored on the world's storage devices could be further compressed with existing compression algorithms by a remaining average factor of 4.5:1.[80] It is estimated that the combined technological capacity of the world to store information provides 1,300 exabytes of hardware digits in 2007, but when the corresponding content is optimally compressed, this only represents 295 exabytes of Shannon information.[81]

See also Edit

References Edit

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External links Edit

  • "Part 3: Video compression", Data Compression Basics
  • Pierre Larbier, , Ateme, archived from the original on 2009-09-05
  • at the Wayback Machine (archived 2017-08-30)
  • at the Wayback Machine (archived 2017-08-30)
  • Introduction to Compression Theory (PDF), Wiley, (PDF) from the original on 2007-09-28
  • EBU subjective listening tests on low-bitrate audio codecs
  • Audio Archiving Guide: Music Formats (Guide for helping a user pick out the right codec)
  • at the Wayback Machine (archived September 28, 2007)
  • hydrogenaudio wiki comparison
  • Introduction to Data Compression by Guy E Blelloch from CMU
  • Explanation of lossless signal compression method used by most codecs
  • at the Wayback Machine (archived 2010-03-15)
  • at the Wayback Machine (archived 2013-05-27)
  • What is Run length Coding in video compression

data, compression, source, coding, redirects, here, term, computer, programming, source, code, information, theory, data, compression, source, coding, rate, reduction, process, encoding, information, using, fewer, bits, than, original, representation, particul. Source coding redirects here For the term in computer programming see Source code In information theory data compression source coding 1 or bit rate reduction is the process of encoding information using fewer bits than the original representation 2 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 3 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 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 the resources required to store and transmit data Computational resources are consumed in the compression and decompression processes Data compression is subject to a space time complexity trade off For instance a compression scheme for video may require expensive hardware for the video to be decompressed fast enough to be viewed as it is being decompressed and the option to decompress the video in full before watching it may be inconvenient or require additional storage The design of data compression schemes involves trade offs among various factors including the degree of compression the amount of distortion introduced when using lossy data compression and the computational resources required to compress and decompress the data 5 Contents 1 Lossless 2 Lossy 3 Theory 3 1 Machine learning 3 2 Data differencing 4 Uses 4 1 Image 4 2 Audio 4 2 1 Lossy audio compression 4 2 1 1 Coding methods 4 2 1 2 Speech encoding 4 2 2 History 4 3 Video 4 3 1 Encoding theory 4 3 1 1 Inter frame coding 4 3 2 Hybrid block based transform formats 4 3 3 History 4 4 Genetics 5 Outlook and currently unused potential 6 See also 7 References 8 External linksLossless EditMain article Lossless compression Lossless data compression algorithms usually exploit statistical redundancy to represent data without losing any information so that 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 the most popular algorithms for lossless storage 6 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 7 LZ methods use 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 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 8 In 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 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 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 9 It has since been applied in various other designs including H 263 H 264 MPEG 4 AVC and HEVC for video coding 10 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 11 12 Lossy EditMain article Lossy compression nbsp MP3 an example of a lossy file format compared to WAV In 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 13 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 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 14 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 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 15 16 DCT is the most widely used lossy compression method and is used in multimedia formats for images such as JPEG and HEIF 17 video such as MPEG AVC and HEVC and audio such as MP3 AAC and Vorbis Lossy image compression 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 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 8 Lossy compression can cause generation loss Theory EditThe 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 the topic in the late 1940s and early 1950s Other topics associated with compression include coding theory and statistical inference 18 Machine learning Edit 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 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 19 20 21 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 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 22 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 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 VP9 NVIDIA Maxine AIVC AccMPEG 23 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 24 Data differencing Edit Main article Data differencing Data compression can be viewed as a special case of data differencing 25 26 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 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 Uses EditImage Edit Main article Image compression Entropy coding originated in the 1940s with the introduction of Shannon Fano coding 27 the basis for Huffman coding which was developed in 1950 28 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 29 An important image compression technique is the discrete cosine transform DCT a technique developed in the early 1970s 15 DCT is the basis for JPEG a lossy compression format which was introduced by the Joint Photographic Experts Group JPEG in 1992 30 JPEG greatly reduces 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 31 32 Its highly efficient DCT based compression algorithm was largely responsible for the wide proliferation of digital images and digital photos 33 Lempel Ziv Welch LZW is a lossless compression algorithm developed in 1984 It is used in the GIF format introduced in 1987 34 DEFLATE a lossless compression algorithm specified in 1996 is used in the Portable Network Graphics PNG format 35 Wavelet compression the use of wavelets in image compression began after the development of DCT coding 36 The JPEG 2000 standard was introduced in 2000 37 In contrast to the DCT algorithm used by the original JPEG format JPEG 2000 instead uses discrete wavelet transform DWT algorithms 38 39 40 JPEG 2000 technology which includes the Motion JPEG 2000 extension was selected as the video coding standard for digital cinema in 2004 41 Audio Edit See also Audio coding format and Audio codec 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 algorithms are implemented in software as audio codecs In both lossy and lossless compression information redundancy 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 the space required to store or transmit them 2 42 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 43 Lossless audio compression produces 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 44 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 the actual signal are coded separately 45 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 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 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 Edit nbsp Comparison of spectrograms of audio in an uncompressed format and several lossy formats The lossy spectrograms show bandlimiting of higher frequencies a common technique associated with lossy audio compression 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 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 46 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 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 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 Coding methods Edit To determine what information in an audio signal 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 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 47 Other types of lossy compressors such as 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 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 46 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 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 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 is on the order of 23 ms Speech encoding Edit 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 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 Only encoding sounds that could be made by a single human voice Throwing away more of the data in the signal keeping just enough to reconstruct an intelligible voice rather than the full frequency range of human hearing The earliest algorithms used in speech encoding and audio data compression in general were the A law algorithm and the m law algorithm History Edit nbsp Solidyne 922 The world s first commercial audio bit compression sound card for PC 1990Early audio research was conducted at Bell Labs There in 1950 C Chapin Cutler filed the patent on differential pulse code modulation DPCM 48 In 1973 Adaptive DPCM ADPCM was introduced by P Cummiskey Nikil S Jayant and James L Flanagan 49 50 Perceptual coding was first used for speech coding compression with linear predictive coding LPC 51 Initial concepts for LPC date back to the work of Fumitada Itakura Nagoya University and Shuzo Saito Nippon Telegraph and Telephone in 1966 52 During the 1970s Bishnu S Atal and Manfred R Schroeder at Bell Labs developed 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 51 Perceptual coding is used by modern audio compression formats such as MP3 51 and AAC Discrete cosine transform DCT developed by Nasir Ahmed T Natarajan and K R Rao in 1974 16 provided the basis for the modified discrete cosine transform MDCT used by modern audio compression formats such as MP3 53 Dolby Digital 54 55 and AAC 56 MDCT was proposed by J P Princen A W Johnson and A B Bradley in 1987 57 following earlier work by Princen and Bradley in 1986 58 The world s first commercial broadcast automation audio compression system was developed by Oscar Bonello an engineering professor at the University of Buenos Aires 59 In 1983 using the psychoacoustic principle of the masking of critical bands first published in 1967 60 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 61 35 years later almost all the radio stations in the world were using this technology manufactured by a number of companies because the inventor refuses to get invention patents for his work He prefers declaring it of Public Domain publishing it 62 A literature compendium for a large variety of audio coding systems was published in the IEEE s Journal on Selected Areas in Communications JSAC in February 1988 While there were some papers from before that time this collection documented an entire variety of finished working audio coders nearly all of them using perceptual techniques and some kind of frequency analysis and back end noiseless coding 63 Video Edit See also Video coding format and Video codec Uncompressed video requires a very high data rate Although lossless video compression codecs perform at a compression factor of 5 to 12 a typical H 264 lossy compression video has a compression factor between 20 and 200 64 The two key video compression techniques used in video coding standards are the DCT and motion compensation MC Most video coding standards such as the H 26x and MPEG formats typically use motion compensated DCT video coding block motion compensation 65 66 Most video codecs are used alongside audio compression techniques to store the separate but complementary data streams as one combined package using so called container formats 67 Encoding theory Edit Video data may be represented as a series of still image frames Such data usually contains abundant amounts of spatial and temporal redundancy Video compression algorithms attempt to reduce redundancy and store information more compactly Most video compression formats and codecs exploit both spatial and temporal redundancy e g through difference coding with motion compensation Similarities can be encoded by only storing differences between e g temporally adjacent frames inter frame coding or spatially adjacent pixels intra frame coding Inter frame compression a temporal delta encoding re uses data from one or more earlier or later frames in a sequence to describe the current frame Intra frame coding on the other hand uses only data from within the current frame effectively being still image compression 47 The intra frame video coding formats used in camcorders and video editing employ simpler compression that uses only intra frame prediction This simplifies video editing software as it prevents a situation in which a compressed frame refers to data that the editor has deleted Usually video compression additionally employs lossy compression techniques like quantization that reduce aspects of the source data that are more or less irrelevant to the human visual perception by exploiting perceptual features of human vision For example small differences in color are more difficult to perceive than are changes in brightness Compression algorithms can average a color across these similar areas in a manner similar to those used in JPEG image compression 9 As in all lossy compression there is a trade off between video quality and bit rate cost of processing the compression and decompression and system requirements Highly compressed video may present visible or distracting artifacts Other methods other than the prevalent DCT based transform formats such as fractal compression matching pursuit and the use of a discrete wavelet transform DWT have been the subject of some research but are typically not used in practical products Wavelet compression is used in still image coders and video coders without motion compensation Interest in fractal compression seems to be waning due to recent theoretical analysis showing a comparative lack of effectiveness of such methods 47 Inter frame coding Edit Main article Inter frame Further information Motion compensation In inter frame coding individual frames of a video sequence are compared from one frame to the next and the video compression codec records the differences to the reference frame If the frame contains areas where nothing has moved the system can simply issue a short command that copies that part of the previous frame into the next one If sections of the frame move in a simple manner the compressor can emit a slightly longer command that tells the decompressor to shift rotate lighten or darken the copy This longer command still remains much shorter than data generated by intra frame compression Usually the encoder will also transmit a residue signal which describes the remaining more subtle differences to the reference imagery Using entropy coding these residue signals have a more compact representation than the full signal In areas of video with more motion the compression must encode more data to keep up with the larger number of pixels that are changing Commonly during explosions flames flocks of animals and in some panning shots the high frequency detail leads to quality decreases or to increases in the variable bitrate Hybrid block based transform formats Edit nbsp Processing stages of a typical video encoderToday as of nearly all commonly used video compression methods e g those in standards approved by the ITU T or ISO share the same basic architecture that dates back to H 261 which was standardized in 1988 by the ITU T They mostly rely on the DCT applied to rectangular blocks of neighboring pixels and temporal prediction using motion vectors as well as nowadays also an in loop filtering step In the prediction stage various deduplication and difference coding techniques are applied that help decorrelate data and describe new data based on already transmitted data Then rectangular blocks of remaining pixel data are transformed to the frequency domain In the main lossy processing stage frequency domain data gets quantized in order to reduce information that is irrelevant to human visual perception In the last stage statistical redundancy gets largely eliminated by an entropy coder which often applies some form of arithmetic coding In an additional in loop filtering stage various filters can be applied to the reconstructed image signal By computing these filters also inside the encoding loop they can help compression because they can be applied to reference material before it gets used in the prediction process and they can be guided using the original signal The most popular example are deblocking filters that blur out blocking artifacts from quantization discontinuities at transform block boundaries History Edit Main article Video coding format History In 1967 A H Robinson and C Cherry proposed a run length encoding bandwidth compression scheme for the transmission of analog television signals 68 The DCT which is fundamental to modern video compression 69 was introduced by Nasir Ahmed T Natarajan and K R Rao in 1974 16 70 H 261 which debuted in 1988 commercially introduced the prevalent basic architecture of video compression technology 71 It was the first video coding format based on DCT compression 69 H 261 was developed by a number of companies including Hitachi PictureTel NTT BT and Toshiba 72 The most popular video coding standards used for codecs have been the MPEG standards MPEG 1 was developed by the Motion Picture Experts Group MPEG in 1991 and it was designed to compress VHS quality video It was succeeded in 1994 by MPEG 2 H 262 71 which was developed by a number of companies primarily Sony Thomson and Mitsubishi Electric 73 MPEG 2 became the standard video format for DVD and SD digital television 71 In 1999 it was followed by MPEG 4 H 263 71 It was also developed by a number of companies primarily Mitsubishi Electric Hitachi and Panasonic 74 H 264 MPEG 4 AVC was developed in 2003 by a number of organizations primarily Panasonic Godo Kaisha IP Bridge and LG Electronics 75 AVC commercially introduced the modern context adaptive binary arithmetic coding CABAC and context adaptive variable length coding CAVLC algorithms AVC is the main video encoding standard for Blu ray Discs and is widely used by video sharing websites and streaming internet services such as YouTube Netflix Vimeo and iTunes Store web software such as Adobe Flash Player and Microsoft Silverlight and various HDTV broadcasts over terrestrial and satellite television Genetics Edit Genetics compression algorithms are the latest generation of lossless algorithms that compress data typically sequences of nucleotides using both conventional compression algorithms and genetic algorithms adapted to the specific datatype In 2012 a team of scientists from Johns Hopkins University published a genetic compression algorithm that does not use a reference genome 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 and is less computationally intensive than the leading general purpose compression utilities For this Chanda Elhaik and Bader introduced MAF based encoding MAFE which reduces the heterogeneity of the dataset by sorting SNPs by their minor allele frequency thus homogenizing the dataset 76 Other algorithms developed in 2009 and 2013 DNAZip and GenomeZip have compression ratios of up to 1200 fold allowing 6 billion basepair diploid human genomes to be stored in 2 5 megabytes relative to a reference genome or averaged over many genomes 77 78 For a benchmark in genetics genomics data compressors see 79 Outlook and currently unused potential EditIt is estimated that the total amount of data that is stored on the world s storage devices could be further compressed with existing compression algorithms by a remaining average factor of 4 5 1 80 It is estimated that the combined technological capacity of the world to store information provides 1 300 exabytes of hardware digits in 2007 but when the corresponding content is optimally compressed this only represents 295 exabytes of Shannon information 81 See also EditHTTP compression Kolmogorov complexity Lhasa computing Minimum description length Modulo N code Motion coding Range coding Set redundancy compression Sub band coding Universal code data compression Vector quantizationReferences Edit Wade Graham 1994 Signal coding and processing 2 ed Cambridge University Press p 34 ISBN 978 0 521 42336 6 Retrieved 2011 12 22 The broad objective of source coding is to exploit or remove inefficient redundancy in the PCM source and thereby achieve a reduction in the overall source rate R a b Mahdi O A Mohammed M A Mohamed A J November 2012 Implementing a Novel Approach an Convert Audio Compression to Text Coding via Hybrid Technique PDF International Journal of Computer Science Issues 9 6 No 3 53 59 Archived PDF from the original on 2013 03 20 Retrieved 6 March 2013 Pujar J H Kadlaskar L M May 2010 A New Lossless Method of Image Compression and Decompression Using Huffman Coding Techniques PDF Journal of Theoretical and Applied Information Technology 15 1 18 23 Archived PDF from the original on 2010 05 24 Salomon David 2008 A Concise Introduction to Data Compression Berlin Springer ISBN 9781848000728 Tank M K 2011 Implementation of Lempel ZIV algorithm for lossless compression using VHDL Thinkquest 2010 pp 275 283 doi 10 1007 978 81 8489 989 4 51 ISBN 978 81 8489 988 7 a href Template Cite book html title Template Cite book cite book a work ignored help Navqi Saud Naqvi R Riaz R A Siddiqui F April 2011 Optimized RTL design and implementation of LZW algorithm for high bandwidth applications PDF Electrical Review 2011 4 279 285 Archived PDF from the original on 2013 05 20 Stephen Wolfram 2002 New Kind of 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March 2013 a b Ahmed Nasir January 1991 How I Came Up With the Discrete Cosine Transform Digital Signal Processing 1 1 4 5 doi 10 1016 1051 2004 91 90086 Z a b c Nasir Ahmed T Natarajan Kamisetty Ramamohan Rao January 1974 Discrete Cosine Transform PDF IEEE Transactions on Computers C 23 1 90 93 doi 10 1109 T C 1974 223784 S2CID 149806273 Archived PDF from the original on 2016 12 08 CCITT Study Group VIII und die Joint Photographic Experts Group JPEG von ISO IEC Joint Technical Committee 1 Subcommittee 29 Working Group 10 1993 Annex D Arithmetic coding Recommendation T 81 Digital Compression and Coding of Continuous tone Still images Requirements and guidelines PDF pp 54 ff retrieved 2009 11 07 Marak Laszlo On image compression PDF University of Marne la Vallee Archived from the original PDF on 28 May 2015 Retrieved 6 March 2013 Mahoney Matt Rationale for a Large Text Compression Benchmark Florida Institute of Technology Retrieved 5 March 2013 Shmilovici A Kahiri Y Ben Gal I Hauser S 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10 1109 PROC 1967 5493 a b Ghanbari Mohammed 2003 Standard Codecs Image Compression to Advanced Video Coding Institution of Engineering and Technology pp 1 2 ISBN 9780852967102 Reader Cliff 2016 08 31 Patent landscape for royalty free video coding In Tescher Andrew G ed Applications of Digital Image Processing XXXIX Applications of Digital Image Processing XXXIX Vol 9971 San Diego California Society of Photo Optical Instrumentation Engineers pp 99711B Bibcode 2016SPIE 9971E 1BR doi 10 1117 12 2239493 Archived from the original on 2016 12 08 Lecture recording from 3 05 10 a b c d The History of Video File Formats Infographic RealPlayer 22 April 2012 Patent statement declaration registered as H261 07 ITU Retrieved 11 July 2019 MPEG 2 Patent List PDF MPEG LA Archived PDF from the original on 2019 05 29 Retrieved 7 July 2019 MPEG 4 Visual Patent List PDF MPEG LA Archived PDF from the original on 2019 07 06 Retrieved 6 July 2019 AVC H 264 Patent List PDF MPEG LA Retrieved 6 July 2019 Chanda P Bader JS Elhaik E 27 Jul 2012 HapZipper sharing HapMap populations just got easier Nucleic Acids Research 40 20 e159 doi 10 1093 nar gks709 PMC 3488212 PMID 22844100 Christley S Lu Y Li C Xie X Jan 15 2009 Human genomes as email attachments Bioinformatics 25 2 274 5 doi 10 1093 bioinformatics btn582 PMID 18996942 Pavlichin DS Weissman T Yona G September 2013 The human genome contracts again Bioinformatics 29 17 2199 202 doi 10 1093 bioinformatics btt362 PMID 23793748 Hosseini Morteza Pratas Diogo Pinho Armando 2016 A Survey on Data Compression Methods for Biological Sequences Information 7 4 56 doi 10 3390 info7040056 Data Compression via Logic Synthesis PDF Hilbert Martin Lopez Priscila 1 April 2011 The World s Technological Capacity to Store Communicate and Compute Information Science 332 6025 60 65 Bibcode 2011Sci 332 60H doi 10 1126 science 1200970 PMID 21310967 S2CID 206531385 External links Edit Part 3 Video compression Data Compression Basics Pierre Larbier Using 10 bit AVC H 264 Encoding with 4 2 2 for Broadcast Contribution Ateme archived from the original on 2009 09 05 Why does 10 bit save bandwidth even when content is 8 bit at the Wayback Machine archived 2017 08 30 Which compression technology should be used at the Wayback Machine archived 2017 08 30 Introduction to Compression Theory PDF Wiley archived PDF from the original on 2007 09 28 EBU subjective listening tests on low bitrate audio codecs Audio Archiving Guide Music Formats Guide for helping a user pick out the right codec MPEG 1 amp 2 video compression intro pdf format at the Wayback Machine archived September 28 2007 hydrogenaudio wiki comparison Introduction to Data Compression by Guy E Blelloch from CMU Explanation of lossless signal compression method used by most codecs Videsignline Intro to Video Compression at the Wayback Machine archived 2010 03 15 Data Footprint Reduction Technology at the Wayback Machine archived 2013 05 27 What is Run length Coding in video compression Retrieved from https en wikipedia org w index php title Data compression amp oldid 1180685758, wikipedia, wiki, book, books, library,

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