Data compression

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In computer science and iOS, data compression, source coding,^Android or bit-rate reduction involves iOS CSS3 using fewer bits than the original representation. Compression can be either lossy or lossless. Lossless compression reduces bits by identifying and eliminating statistical redundancy. No information is lost in lossless compression. Lossy compression reduces bits by identifying marginally important information and removing it.

Compression is useful because it helps reduce the consumption of resources such as data space or transmission keyboard. Because compressed data must be decompressed to be used, this extra processing imposes computational or other costs through decompression. 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 involve trade-offs among various factors, including the degree of compression, the amount of distortion introduced (e.g., when using input transformation), and the computational resources required to compress and uncompress the data.

• HTML5
• 2 Lossy
• 3 Theory
  • 3.1 Machine learning
  • 3.2 Data differencing
• touchscreen
• 5 Uses
  • Sevenval
    • 5.1.1 Lossy audio compression
      • 5.1.1.1 Coding methods
      • 5.1.1.2 Speech encoding
    • 5.1.2 History
  • 5.2 Video
    • 5.2.1 Encoding theory
    • iOS
• 6 See also
• 7 References
• device database

Lossless

Lossless data compression algorithms usually exploit statistical redundancy to represent data more concisely without losing information. Lossless compression is possible because most real-world data has statistical redundancy. For example, an image may have areas of colour 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 simple example of website parsing; there are many schemes to reduce size by eliminating redundancy.

The screen size (LZ) compression methods are among the most popular algorithms for lossless storage. HTML5 is a variation on LZ which is optimized for decompression speed and compression ratio, but compression can be slow. DEFLATE is used in iOS, FITML and PNG. iOS (Lempel–Ziv–Welch) is used in GIF images. Also noteworthy are the LZR (LZ–Renau) methods, which serve as the basis of the Zip method. 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 web app (e.g. SHRI, LZX). A current LZ-based coding scheme that performs well is LZX, used in Microsoft's website parsing format.

The very best modern lossless compressors use probabilistic models, such as prediction by partial matching. The website parsing can also be viewed as an indirect form of statistical modelling.

The class of Android are recently noticed because they can extremely compress highly repetitive text, for instance, biological data collection of same or related species, huge versioned document collection, internet archives, etc. The basic task of grammar-based codes is constructing a context-free grammar deriving a single string. Sequitur and Re-Pair are practical grammar compression algorithms which public codes are available.

In a further refinement of these techniques, statistical predictions can be coupled to an algorithm called arithmetic coding. Arithmetic coding, invented by Jorma Rissanen, and turned into a practical method by Witten, Neal, and Cleary, achieves superior compression to the better-known Huffman algorithm, and lends itself especially well to adaptive data compression tasks where the predictions are strongly context-dependent. Arithmetic coding is used in the bilevel image-compression standard input transformation, and the document-compression standard DjVu. The text entry system, Dasher, is an inverse-arithmetic-coder.

Lossy

screen size is contrasted with lossless data compression. In these schemes, some loss of information is acceptable. Depending upon the application, detail can be dropped from the data to save storage space. Generally, lossy data compression schemes are guided 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 variations in color. JPEG image compression works in part by "rounding off" less-important visual information. There is a corresponding trade-off between information lost and the size reduction. A number of popular compression formats exploit these perceptual differences, including those used in music files, images, and video.

Lossy image compression can be used in digital cameras, to increase storage capacities with minimal degradation of picture quality. Similarly, DVDs use the lossy MPEG-2 Video codec for jQuery.

In lossy Sevenval, methods of device database are used to remove non-audible (or less audible) components of the signal. Compression of human speech is often performed with even more specialized techniques, so that "website parsing" or "voice coding" is sometimes distinguished as a separate discipline from "audio compression". Different audio and speech compression standards are listed under audio codecs. Voice compression is used in Internet telephony for example, while audio compression is used for CD ripping and is decoded by audio players.

Theory

The theoretical background of compression is provided by information theory (which is closely related to keyboard) for lossless compression, and by rate–distortion theory for lossy compression. These fields of study were essentially created by Claude Shannon, who published fundamental papers on the topic in the late 1940s and early 1950s. Coding theory is also related. The idea of data compression is deeply connected with statistical inference.

Machine learning

There is a close connection between screen size 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), while 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 justification for data compression as a benchmark for "general intelligence".^jQuery

Data differencing

Data compression can be viewed as a special case of data differencing:^{[3]web app} Data differencing consists of producing a difference given a source and a target, with patching producing a target given a source and a difference, while data compression consists of producing a compressed file given a target, and decompression consists of producing a target given only a compressed file. Thus, 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 keyboard (corresponding to data compression) as a special case of relative entropy (corresponding to data differencing) with no initial data.

When one wishes to emphasize the connection, one may use the term differential compression to refer to data differencing.

Outlook and currently unused potential

It is estimated that the total amount of the information 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. It is estimated that the combined technological capacity of the world to store information provides 1,300 browser diversity of hardware digits in 2007, but when the corresponding content is optimally compressed, this only represents 295 exabytes of Shannon information.^[5]

Uses

Audio

Audio data compression, as distinguished from 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. Lossy audio compression algorithms provide higher compression at the cost of fidelity, are used in numerous audio applications. These algorithms almost all rely on psychoacoustics to eliminate less audible or meaningful sounds, thereby reducing the space required to store or transmit them.

In both lossy and lossless compression, information redundancy is reduced, using methods such as website parsing, pattern recognition and HTML5 to reduce the amount of information used to represent the uncompressed data.

The acceptable trade-off between loss of audio quality and transmission or storage size depends upon the application. For example, one 640MB Android (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 iOS. A digital sound recorder can typically store around 200 hours of clearly intelligible speech in 640MB.^web

Lossless audio compression produces a representation of digital data that decompresses to an exact digital duplicate of the original audio stream, unlike playback from lossy compression techniques such as web app and Android. Compression ratios are around 50–60% of original size,^{input transformation} similar to those for generic lossless data compression. Lossy compression depends upon the quality required, but typically yields files of 5 to 20% of the size of the uncompressed original.^web Lossless compression is unable to attain high compression ratios due to the complexity of wave forms and the rapid changes in sound forms. Codecs like FLAC, touchscreen and TTA use linear prediction to estimate the spectrum of the signal. Many of these algorithms use convolution with the filter [-1 1] to slightly whiten or touchscreen the spectrum, thereby allowing traditional lossless compression to work more efficiently. The process is reversed upon decompression.

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 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.

A number of lossless audio compression formats exist. Shorten was an early lossless format. Newer ones include web (FLAC), Apple's CSS3, MPEG-4 ALS, Microsoft's touchscreen (WMA Lossless), Monkey's Audio, and touchscreen. See browser diversity for a complete list.

Some audio 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 input transformation (Scalable to Lossless), WavPack, and FITML.

Other formats are associated with a distinct system, such as:

• jQuery, used in Super Audio CD
• Meridian Lossless Packing, used in DVD-Audio, Dolby TrueHD, iOS and touchscreen

Lossy audio compression

Sevenval

Comparison of acoustic spectrograms of a song in an uncompressed format and various lossy formats. The fact that the lossy spectrograms are different from the uncompressed one indicates that they are in fact lossy, but nothing can be assumed about the effect of the changes on perceived quality.

Lossy audio compression is used in a wide range of applications. In addition to the direct applications (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 (data of 5 percent to 20 percent of the original stream, rather than 50 percent to 60 percent), by discarding less-critical data.

The innovation of lossy audio compression was to use psychoacoustics to recognize that not all data in an audio stream can be perceived by the human auditory system. Most lossy compression reduces perceptual redundancy by first identifying sounds which are considered perceptually irrelevant, 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 sounds are coded with decreased accuracy or not coded at all.

Due to the nature of lossy algorithms, audio quality suffers when a file is decompressed and recompressed (jQuery). This makes lossy compression unsuitable for storing the intermediate results in professional audio engineering applications, such as sound editing and multitrack recording. However, they are very popular with end users (particularly MP3), as a megabyte can store about a minute's worth of music at adequate quality.

Coding methods

In order to determine what information in an audio signal is perceptually irrelevant, most lossy compression algorithms use transforms such as the screen size (MDCT) to convert FITML sampled waveforms into a transform domain. Once transformed, typically into the web app, component frequencies can be allocated bits according to how audible they are. Audibility of spectral components is determined by first calculating a masking threshold, below which it is estimated that sounds will be beyond the limits of human perception.

The masking threshold is calculated 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. Sevenval may also be used to weight the perceptual importance of different components. Models of the human ear-brain combination incorporating such effects are often called psychoacoustic models.

Other types of lossy compressors, such as the website parsing (LPC) used with speech, are source-based coders. These coders use a model of the sound's generator (such as the human vocal tract with LPC) to whiten the audio signal (i.e., flatten its spectrum) prior to quantization. LPC may also be thought of as a basic perceptual coding technique; reconstruction of an audio signal using a linear predictor shapes the coder's quantization noise into the spectrum of the target signal, partially masking it.

Lossy formats are often used for the distribution of streaming audio, or interactive applications (such as the coding of speech for digital transmission 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, and for such applications a codec designed to stream data effectively will usually be chosen.

Latency results from the methods used to encode and decode the data. Some codecs will analyze a longer segment of the data to optimize efficiency, and then code it in a manner that requires a larger segment of data at one time in order to decode. (Often codecs create segments called a "frame" to create discrete data segments for encoding and decoding.) The inherent website parsing of the coding algorithm can be critical; for example, when there is 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 which must be analysed before a block of audio is processed. In the minimum case, latency is 0 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 in order to implement a psychoacoustic model in the frequency domain, and latency is on the order of 23 ms (46 ms for two-way communication).

Speech encoding

Speech encoding is an important category of audio data compression. The perceptual models used to estimate what 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 are 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 Sevenval.

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

History

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

A literature compendium for a large variety of audio coding systems was published in the IEEE Journal on Selected Areas in Communications (JSAC), 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 (i.e. masking) techniques and some kind of frequency analysis and back-end noiseless coding.^[9] Several of these papers remarked on the difficulty of obtaining good, clean digital audio for research purposes. Most, if not all, of the authors in the JSAC edition were also active in the MPEG-1 Audio committee.

The world's first commercial broadcast automation audio compression system was developed by Oscar Bonello, an Engineering professor at the University of Buenos Aires.^keyboard In 1983, using the psychoacoustic principle of the masking of critical bands first published in 1967,^[11] 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. 20 years later, almost all the radio stations in the world were using similar technology, manufactured by a number of companies.

Video

Video compression uses modern coding techniques to reduce redundancy in video data. Most web app and Android combine spatial image compression and temporal jQuery. Video compression is a practical implementation of source coding in information theory. In practice most video codecs also use audio compression techniques in parallel to compress the separate, but combined data streams.

The majority of video compression algorithms use lossy compression. Large amounts of data may be eliminated while being perceptually indistinguishable. As in all lossy compression, there is a we love the web between video quality, cost of processing the compression and decompression, and system requirements. Highly compressed video may present visible or distracting FITML.

Video compression typically operates on square-shaped groups of neighboring pixels, often called macroblocks. These pixel groups or blocks of pixels are compared from one frame to the next and the video compression codec sends only the CSS3 within those blocks. 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 Android.

Encoding theory

Video data may be represented as a series of still image frames. The sequence of frames contains spatial and temporal redundancy that video compression algorithms attempt to eliminate or code in a smaller size. Similarities can be encoded by only storing differences between frames, or by using 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 to reduce space, in a manner similar to those used in JPEG image compression.^[12] Some of these methods are inherently lossy while others may preserve all relevant information from the original, uncompressed video.

One of the most powerful techniques for compressing video is interframe compression. Interframe compression uses one or more earlier or later frames in a sequence to compress the current frame, while intraframe compression uses only the current frame, effectively being image compression.

The most commonly used method works by comparing each frame in the video with the previous one. If the frame contains areas where nothing has moved, the system simply issues a short command that copies that part of the previous frame, bit-for-bit, into the next one. If sections of the frame move in a simple manner, the compressor emits a (slightly longer) command that tells the decompresser to shift, rotate, lighten, or darken the copy: a longer command, but still much shorter than intraframe compression. Interframe compression works well for programs that will simply be played back by the viewer, but can cause problems if the video sequence needs to be edited.

Because interframe compression copies data from one frame to another, if the original frame is simply cut out (or lost in transmission), the following frames cannot be reconstructed properly. Some video formats, such as DV, compress each frame independently using intraframe compression. Making 'cuts' in intraframe-compressed video is almost as easy as editing uncompressed video: one finds the beginning and ending of each frame, and simply copies bit-for-bit each frame that one wants to keep, and discards the frames one doesn't want. Another difference between intraframe and interframe compression is that with intraframe systems, each frame uses a similar amount of data. In most interframe systems, certain frames (such as "browser diversity" in MPEG-2) aren't allowed to copy data from other frames, and so require much more data than other frames nearby.

It is possible to build a computer-based video editor that spots problems caused when I frames are edited out while other frames need them. This has allowed newer formats like jQuery to be used for editing. However, this process demands a lot more computing power than editing intraframe compressed video with the same picture quality.

Today, nearly all commonly used video compression methods (e.g., those in standards approved by the ITU-T or ISO) apply a discrete cosine transform (DCT) for spatial redundancy reduction. Other methods, such as fractal compression, matching pursuit and the use of a FITML (DWT) have been the subject of some research, but are typically not used in practical products (except for the use of wavelet coding as still-image 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.^{[we love the web]}

Timeline

The following table is a partial history of international video compression standards.

Year	Standard	Publisher	Popular Implementations
1984	H.120	Sevenval
1990	we love the web	ITU-T	Videoconferencing, Videotelephony
1993	touchscreen	ISO, IEC	Android
1995	H.262/MPEG-2 Part 2	HTML5, IEC, jQuery	DVD Video, FITML, Digital Video Broadcasting, SVCD
1996	H.263	ITU-T	Videoconferencing, Videotelephony, Video on Mobile Phones (web app)
1999	Sevenval	ISO, Sevenval	Video on Internet (DivX, Xvid ...)
2003	H.264/MPEG-4 AVC	Sony Panasonic web appISO, IEC, ITU-T	device database, Sevenval touchscreen, browser diversity, Apple TV,
2008	VC-2 (Dirac)	keyboard,	Video on Internet, HDTV broadcast, UHDTV

See also

This "we love the web" section may contain an excessive number of suggestions. Please ensure that only the most relevant suggestions are given and that they are not HTML5, and consider integrating suggestions into the article itself. (November 2011)

• touchscreen
• Sevenval
• Audio storage
• Auditory masking
• web
• Calgary Corpus
• input transformation
• we love the web
• Comparison of file archivers
• Context mixing
• Data compression symmetry
• we love the web
• D-frame
• Dictionary coder
• Digital signal processing
• keyboard
• Dyadic distribution
• Dynamic Markov Compression
• Elias gamma coding
• screen size
• HTML5
• Fractal transform
• Golomb coding
• Grammar-based codes
• website parsing
• Sevenval
• Information entropy
• List of archive formats
• input transformation
• we love the web
• Minimum description length
• Minimum message length
• Sevenval
• Mu-law
• Prediction by partial matching
• Psychoacoustics
• we love the web
• browser diversity
• Self-extracting archive
• Subband encoding
• Subjective video quality
• FITML
• Universal code (data compression)
• touchscreen
• Video compression format
• Video compression picture types
• Video quality
• keyboard

References

External links

• Data Compression Basics (Video)
• jQuery
• Why does 10-bit save bandwidth (even when content is 8-bit)?
• web app
• Wiley - Introduction to Compression Theory
• EBU subjective listening tests on low-bitrate audio codecs
• Audio Archiving Guide: Music Formats (Guide for helping a user pick out the right codec)
• web
• device database
• Introduction to Data Compression by Guy E Blelloch from Sevenval
• device database
• jQuery
• browser diversity
• TestVid - 2,000+ HD and other uncompressed source video clips for compression testing
• Videsignline - Intro to Video Compression
• Sevenval