Data compression

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. DEFLATE, a lossless compression algorithm specified in 1996, is used in the Portable Network Graphics (PNG) format.

In computing, Deflate is a lossless data compression file format that uses a combination of LZSS and Huffman coding.

Lossless compression reduces bits by identifying and eliminating statistical redundancy.

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.

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.

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.

In the late 1980s, digital images became more common, and standards for lossless image compression emerged. DCT is the most widely used lossy compression method, and is used in multimedia formats for images (such as JPEG and HEIF), video (such as MPEG, AVC and HEVC) and audio (such as MP3, AAC and Vorbis). Lossy image compression is used in digital cameras, to increase storage capacities.

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

It has since been applied in various other designs including H.263, H.264/MPEG-4 AVC and HEVC for video coding. DCT is the most widely used lossy compression method, and is used in multimedia formats for images (such as JPEG and HEIF), video (such as MPEG, AVC and HEVC) and audio (such as MP3, AAC and Vorbis).

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.

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.

Most forms of lossy compression are based on transform coding, especially the discrete cosine transform (DCT).

Transform coding is a type of data compression for "natural" data like audio signals or photographic images.

Most forms of lossy compression are based on transform coding, especially the discrete cosine transform (DCT).

The DCT, first proposed by Nasir Ahmed in 1972, is the most widely used transformation technique in signal processing and data compression.

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.

DCT is the most widely used lossy compression method, and is used in multimedia formats for images (such as JPEG and HEIF), video (such as MPEG, AVC and HEVC) and audio (such as MP3, AAC and Vorbis).

Advanced Audio Coding (AAC) is an audio coding standard for lossy digital audio compression.

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

Like previous H.26x standards, H.263 is based on discrete cosine transform (DCT) video compression.

DCT is the most widely used lossy compression method, and is used in multimedia formats for images (such as JPEG and HEIF), video (such as MPEG, AVC and HEVC) and audio (such as MP3, AAC and Vorbis).

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.

DCT is the most widely used lossy compression method, and is used in multimedia formats for images (such as JPEG and HEIF), video (such as MPEG, AVC and HEVC) and audio (such as MP3, AAC and Vorbis).

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

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.

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.

The DCT is the most widely used data compression transformation, the basis for most digital media standards (image, video and audio) and commonly used in digital signal processing.

The theoretical basis for compression is provided by information theory and, more specifically, algorithmic information theory for lossless compression and rate–distortion theory for lossy compression.

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

Other topics associated with compression include coding theory and statistical inference.

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

Lossy image compression is used in digital cameras, to increase storage capacities.

Practical digital cameras were enabled by advances in data compression, due to the impractically high memory and bandwidth requirements of uncompressed images and video.

Entropy coding started in the 1940s with the introduction of Shannon–Fano coding, the basis for Huffman coding which was developed in 1950.

In the field of data compression, Shannon–Fano coding, named after Claude Shannon and Robert Fano, is a technique for constructing a prefix code based on a set of symbols and their probabilities (estimated or measured).

Similarly, DVDs, Blu-ray and streaming video use the lossy video coding format.

Practical streaming media was only made possible with advances in data compression, due to the impractically high bandwidth requirements of uncompressed media.

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).

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

It can be used in data compression software applications.

DEFLATE, a lossless compression algorithm specified in 1996, is used in the Portable Network Graphics (PNG) format.

The motivation for creating the PNG format was the realization, in early 1995, that the Lempel–Ziv–Welch (LZW) data compression algorithm used in the Graphics Interchange Format (GIF) format was patented by Unisys.