Data compression

Lossless compression reduces bits by identifying and eliminating statistical redundancy. Lossless data compression algorithms usually exploit statistical redundancy to represent data without losing any information, so that the process is reversible.

Data compression is a way to reduce or eliminate unwanted redundancy, while checksums are a way of adding desired redundancy for purposes of error detection when communicating over a noisy channel of limited capacity.

Compression can be either lossy or lossless.

In information technology, lossy compression or irreversible compression is the class of data encoding methods that uses inexact approximations and partial data discarding to represent the content.

DEFLATE is a variation on LZ optimized for decompression speed and compression ratio, but compression can be slow.

In computing, Deflate is a lossless data compression algorithm and associated file format that uses a combination of the LZ77 algorithm and Huffman coding.

In the context of data transmission, it is called source coding; encoding done at the source of the data before it is stored or transmitted.

It may also be an analog signal such as a phone call or a video signal, digitized into a bit-stream, for example, using pulse-code modulation (PCM) or more advanced source coding (analog-to-digital conversion and data compression) schemes.

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.

Grammar-based codes or Grammar-based compression are compression algorithms based on the idea of constructing a context-free grammar (CFG) for the string to be compressed.

The strongest modern lossless compressors use probabilistic models, such as prediction by partial matching.

Prediction by partial matching (PPM) is an adaptive statistical data compression technique based on context modeling and prediction.

It has since been applied in various other designs including H.263, H.264/MPEG-4 AVC and HEVC for video coding.

In comparison to AVC, HEVC offers from 25% to 50% better data compression at the same level of video quality, or substantially improved video quality at the same bit rate.

In the late 1980s, digital images became more common, and standards for lossless image compression emerged.

Image compression is a type of data compression applied to digital images, to reduce their cost for storage or transmission.

The Burrows–Wheeler transform can also be viewed as an indirect form of statistical modelling.

The Burrows–Wheeler transform is an algorithm used to prepare data for use with data compression techniques such as bzip2.

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.

Along with Abraham Lempel and Jacob Ziv, he developed the lossless Lempel–Ziv–Welch (LZW) compression algorithm, which was published in 1984.

Similarly, DVDs use the lossy MPEG-2 video coding format for video compression.

It describes a combination of lossy video compression and lossy audio data compression methods, which permit storage and transmission of movies using currently available storage media and transmission bandwidth.

The theoretical background of compression is provided by information theory (which is closely related to algorithmic information theory) for lossless compression and rate–distortion theory for lossy compression.

It was originally proposed by Claude E. Shannon in 1948 to find fundamental limits on signal processing and communication operations such as data compression, in a landmark paper entitled "A Mathematical Theory of Communication".

Coding theory is also related to this.

Codes are used for data compression, cryptography, error detection and correction, data transmission and data storage.

Data compression is subject to a space–time complexity trade-off.

If data is stored uncompressed, it takes more space but access takes less time than if the data were stored compressed (since compressing the data reduces the amount of space it takes, but it takes time to run the decompression algorithm).

In signal processing, data compression, source coding, or bit-rate reduction involves encoding information using fewer bits than the original representation.

(Source coding), including audio compression, image compression, and video compression.

In lossy audio compression, methods of psychoacoustics are used to remove non-audible (or less audible) components of the audio signal.

Processing methods and application areas include storage, data compression, music information retrieval, speech processing, localization, acoustic detection, transmission, noise cancellation, acoustic fingerprinting, sound recognition, synthesis, and enhancement (e.g. equalization, filtering, level compression, echo and reverb removal or addition, etc.).

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. Lossless audio compression produces a representation of digital data that decompress to an exact digital duplicate of the original audio stream, unlike playback from lossy compression techniques such as Vorbis and MP3.

In the aspects of MP3 pertaining to audio compression—the aspect of the standard most apparent to end-users (and for which is it best known)—MP3 uses lossy data-compression to encode data using inexact approximations and the partial discarding of data.

Lossless audio compression produces a representation of digital data that decompress to an exact digital duplicate of the original audio stream, unlike playback from lossy compression techniques such as Vorbis and MP3.

The project produces an audio coding format and software reference encoder/decoder (codec) for lossy audio compression.

Other practical grammar compression algorithms include Sequitur and Re-Pair.

It can be used in data compression software applications.

A number of popular compression formats exploit these perceptual differences, including psychoacoustics for sound, and psychovisuals for images and video.

Similar concepts are applied in audio compression, where sound frequencies inaudible to humans are bandstop filtered.

Lossless data compression algorithms usually exploit statistical redundancy to represent data without losing any information, so that the process is reversible.

Some example classes are search algorithms, sorting algorithms, merge algorithms, numerical algorithms, graph algorithms, string algorithms, computational geometric algorithms, combinatorial algorithms, medical algorithms, machine learning, cryptography, data compression algorithms and parsing techniques.

Newer ones include Free Lossless Audio Codec (FLAC), Apple's Apple Lossless (ALAC), MPEG-4 ALS, Microsoft's Windows Media Audio 9 Lossless (WMA Lossless), Monkey's Audio, TTA, and WavPack.

WavPack is a free and open-source lossless audio compression format.

An early example of the use of arithmetic coding was in an optional (but not widely used) feature of the JPEG image coding standard.

In video compression MCUs are called macroblocks.

Codecs like FLAC, Shorten, and TTA use linear prediction to estimate the spectrum of the signal.

It is a form of data compression of files and is used to losslessly compress CD-quality audio files (44.1 kHz 16-bit stereo PCM).

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.

The bit layout of the audio data (excluding metadata) is called the audio coding format and can be uncompressed, or compressed to reduce the file size, often using lossy compression.