Information about Data Compression
“Source coding” redirects here. For the term in computer programming, see Source code.
In computer science and information theory, data compression or source coding is the process of encoding information using fewer bits (or other information-bearing units) than an un-encoded representation would use through use of specific encoding schemes. For example, this article could be encoded with fewer bits if one were to accept the convention that the word "compression" be encoded as "comp." One popular instance of compression with which many computer users are familiar is the ZIP file format, which, as well as providing compression, acts as an archiver, storing many files in a single output file.
As with any communication, compressed data communication only works when both the sender and receiver of the information understand the encoding scheme. For example, this text makes sense only if the receiver understands that it is intended to be interpreted as characters representing the English language. Similarly, compressed data can only be understood if the decoding method is known by the receiver.
Compression is useful because it helps reduce the consumption of expensive resources, such as hard disk space or transmission bandwidth. On the downside, compressed data must be decompressed to be viewed (or heard), and this extra processing may be detrimental to some applications. For instance, a compression scheme for video may require expensive hardware for the video to be decompressed fast enough to be viewed as it's being decompressed (the option of decompressing the video in full before watching it may be inconvenient, and requires storage space for the decompressed video). The design of data compression schemes therefore involves trade-offs among various factors, including the degree of compression, the amount of distortion introduced (if using a lossy compression scheme), and the computational resources required to compress and uncompress the data.
Lossless vs. Lossy Compression
Lossless compression algorithms usually exploit statistical redundancy in such a way as to represent the sender's data more concisely, but nevertheless perfectly. Lossless compression is possible because most real-world data has statistical redundancy. For example, in English text, the letter 'e' is much more common than the letter 'z', and the probability that the letter 'q' will be followed by the letter 'z' is very small.Another kind of compression, called lossy data compression, is possible if some loss of fidelity is acceptable. For example, a person viewing a picture or television video scene might not notice if some of its finest details are removed or not represented perfectly (i.e. may not even notice compression artifacts). Similarly, two clips of audio may be perceived as the same to a listener even though one is missing details found in the other. Lossy data compression algorithms introduce relatively minor differences and represent the picture, video, or audio using fewer bits.
Lossless compression schemes are reversible so that the original data can be reconstructed, while lossy schemes accept some loss of data in order to achieve higher compression.
However, lossless data compression algorithms will always fail to compress some files; indeed, any compression algorithm will necessarily fail to compress any data containing no discernible patterns. Attempts to compress data that has been compressed already will therefore usually result in an expansion, as will attempts to compress encrypted data.
In practice, lossy data compression will also come to a point where compressing again does not work, although an extremely lossy algorithm, which for example always removes the last byte of a file, will always compress a file up to the point where it is empty.
An example of lossless vs. lossy compression is the following string:
- 888883333333
- 8[5]3[7].
Interpreted as, "5 eights, 7 threes", the original string is perfectly recreated, just written in a smaller form. In a lossy system, using
- 83
instead, the original data is lost, at the benefit of a smaller filesize.
Applications
The above is a very simple example of run-length encoding, wherein large runs of consecutive identical data values are replaced by a simple code with the data value and length of the run. This is an example of lossless data compression. It is often used to optimize disk space on office computers, or better use the connection bandwidth in a computer network. For symbolic data such as spreadsheets, text, executable programs, etc., losslessness is essential because changing even a single bit cannot be tolerated (except in some limited cases).For visual and audio data, some loss of quality can be tolerated without losing the essential nature of the data. By taking advantage of the limitations of the human sensory system, a great deal of space can be saved while producing an output which is nearly indistinguishable from the original. These lossy data compression methods typically offer a three-way tradeoff between compression speed, compressed data size and quality loss.
Lossy image compression is used in digital cameras, greatly increasing their storage capacities while hardly degrading picture quality at all. Similarly, DVDs use the lossy MPEG-2 codec for video compression.
In lossy audio compression, methods of psychoacoustics 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 "speech compression" or "voice coding" is sometimes distinguished as a separate discipline than "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 algorithmic information theory) and by rate-distortion theory. These fields of study were essentially created by Claude Shannon, who published fundamental papers on the topic in the late 1940s and early 1950s. Doyle and Carlson (2000) wrote that data compression "has one of the simplest and most elegant design theories in all of engineering". Cryptography and coding theory are also closely related. The idea of data compression is deeply connected with statistical inference.Many lossless data compression systems can be viewed in terms of a four-stage model. Lossy data compression systems typically include even more stages, including, for example, prediction, frequency transformation, and quantization.
The Lempel-Ziv (LZ) compression methods are among the most popular algorithms for lossless storage. DEFLATE is a variation on LZ which is optimized for decompression speed and compression ratio, although compression can be slow. DEFLATE is used in PKZIP, gzip and PNG. LZW (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 utilize 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 (e.g. SHRI, LZX). A current LZ-based coding scheme that performs well is LZX, used in Microsoft's CAB format.
The very best compressors use probabilistic models whose predictions are 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 JBIG, and the document-compression standard DjVu. The text entry system, Dasher, is an inverse-arithmetic-coder.
Matt Mahoney, one of the 3 founders of the Hutter Prize, claims that "Compression is Equivalent to General Intelligence" [1].
Comparative
Independent comparison of different methods of data compression (Results & Softwares, in French. Airelle, 2007). Numbers in parenthesis are the rank of the method of compression for the category of file specified above.- Text files, such as .htm or .txt, can be hard compressed.
- Some files are already compressed (e.g. .mp3 or .zip), so the compression rate of such files is poor. Due to the addition of header data, often there are diminishing returns in such compression, causing the file to actually be slightly larger upon storage.
- To be more representative of the performance, the global score (/20) is calculated with a non-parametric formula after the sum of the ranks (1 to 20) for each of the 20 tested methods.
| Comparison of different methods of data compression | |||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Files | *.avi | *.dll | *.doc | *.exe | *.gif | *.htm | *.jpg | *.mp3 | *.mpg | *.txt | *.wav | *.zip | Notation | TOTAL | |
| Number of files | 16 | 26 | 138 | 24 | 246 | 79 | 44 | 29 | 8 | 36 | 8 | 1 | 19 | 674 | |
| Initial size | 5,261,152 | 5,254,220 | 5,254,656 | 5,254,056 | 5,246,209 | 5,261,187 | 5,246,116 | 5,250,432 | 5,257,720 | 5,257,876 | 5,253,436 | 5,256,024 | 5,262,680 | 68,315,764 | |
| 7z | 4,524,067 (2) | 1,543,179 (3) | 147,690 (3) | 3,910,541 (3) | 4 620 354 (1) | 341,996 (4) | 4,770,061 (4) | 5,053,813 (2) | 4,879,067 (5) | 4,258,863 (3) | 1,270,884 (3) | 3,670,225 (5) | 5,226,742 (14) | 16/20 | 44,217,482 |
| arj | 4,696,659 (9) | 2,160,530 (15) | 1,018,050 (17) | 4,130,505 (11) | 4,702,449 (12) | 898,370 (17) | 4,803,740 (11) | 5,108,093 (17) | 4,910,699 (16) | 4,606,736 (15) | 1,875,329 (16) | 4,450,535 (12) | 5,223,905 (13) | 6.1/20 | 48,585,600 |
| bh | 4,703,291 (12) | 2,156,986 (12) | 1,010,284 (15) | 4,128,594 (9) | 4,693,021 (9) | 889,650 (15) | 4,806,914 (13) | 5,105,811 (13) | 4,904,209 (11) | 4,601,545 (13) | 1,848,972 (13) | 4,451,648 (15) | 5,201,639 (4) | 7.5/20 | 48,502,564 |
| bz2 | 4,720,926 (18) | 2,095,832 (7) | 573,721 (5) | 4,273,885 (18) | 4,896,084 (18) | 645,243 (5) | 4,743,918 (2) | 5,069,593 (4) | 4,888,293 (7) | 4,444,829 (5) | 1,531,448 (6) | 3,771,508 (7) | 5,238,677 (16) | 11.7/20 | 46,893,957 |
| bza | 4,639,340 (6) | 2,166,940 (17) | 987,806 (11) | 4,231,254 (17) | 4,878,327 (17) | 783,188 (8) | 4,787,973 (7) | 5,076,189 (5) | 4,873,810 (2) | 4,618,970 (17) | 1,516,326 (5) | 3,770,938 (6) | 5,227,572 (15) | 9.8/20 | 47,558,633 |
| cab | 4,701,113 (11) | 2,148,386 (10) | 893,796 (7) | 4,127,044 (8) | 4,678,810 (5) | 842,129 (10) | 4,798,500 (8) | 5,099,787 (8) | 4,900,314 (10) | 4,584,969 (8) | 1,846,233 (12) | 4,451,857 (18) | 5,201,717 (5) | 10.8/20 | 48,274,655 |
| gza | 4,703,371 (13) | 2,157,116 (13) | 1,001,990 (13) | 4,126,436 (7) | 4,693,136 (10) | 874,444 (12) | 4,803,739 (10) | 5,105,765 (12) | 4,904,249 (12) | 4,597,720 (11) | 1,840,188 (11) | 4,451,638 (14) | 5,201,436 (3) | 9.2/20 | 48,461,228 |
| j | 4,678,506 (8) | 1,914,777 (5) | 703,722 (6) | 4,057,445 (5) | 4,681,437 (6) | 691,916 (6) | 4,805,059 (12) | 5,092,070 (7) | 4,898,847 (8) | 4,326,394 (4) | 1,629,228 (8) | 3,594,954 (4) | 5,215,150 (12) | 13/20 | 46,289,505 |
| jar | 4,704,088 (14) | 2,158,273 (14) | 1,017,205 (16) | 4,129,816 (10) | 4,705,456 (13) | 893,622 (16) | 4,809,136 (16) | 5,107,254 (15) | 4,904,615 (13) | 4,603,367 (14) | 1,849,394 (14) | 4,451,718 (16) | 5,202,611 (8) | 6.2/20 | 48,536,555 |
| lha | 4,711,090 (16) | 2,215,476 (18) | 1,020,194 (18) | 4,204,071 (15) | 4,830,501 (15) | 913,845 (18) | 4,918,792 (19) | 5,206,933 (19) | 5,066,716 (19) | 4,802,049 (19) | 1,895,771 (17) | 4,447,253 (10) | 5,263,136 (18) | 6.7/20 | 49,495,827 |
| lzh | 4,711,090 (16) | 2,215,476 (18) | 1,066,340 (19) | 4,143,461 (14) | 4,819,157 (14) | 971,166 (19) | 4,816,349 (18) | 5,107,584 (16) | 4,924,974 (18) | 4,635,416 (18) | 1,945,961 (19) | 4,449,756 (11) | 5,212,837 (11) | 5.3/20 | 49,019,567 |
| pkz | 4,899,083 (20) | 2,354,373 (20) | 1,173,097 (20) | 4,401,289 (20) | 5,120,590 (19) | 1,018,250 (20) | 5,162,114 (20) | 5,253,006 (20) | 5,203,747 (20) | 5,076,577 (20) | 2,084,290 (20) | 5,027,854 (20) | 5,264,213 (19) | 0.2/20 | 52,038,483 |
| rar | 4,634,009 (5) | 1,693,150 (4) | 173,313 (4) | 3,948,241 (4) | 4,639,881 (4) | 318,269 (3) | 4,780,095 (6) | 5,081 085 (6) | 4,887,973 (6) | 4,258,775 (2) | 1,318,381 (4) | 2,657,731 (3) | 5,202,579 (7) | 15.5/20 | 43,593,482 |
| rk | 4,589,894 (3) | 1,474,339 (2) | 132,629 (1) | 3,866,814 (1) | 4,628,017 (3) | 257,588 (1) | 4,434,701 (1) | 5,017,545 (1) | 4,787,286 (1) | 4,498,992 (6) | 1,168,720 (1) | 1,659,771 (1) | 5,183,337 (1) | 18.2/20 | 41,699,633 |
| rs | 4,625,725 (4) | 2,137,145 (9) | 937,954 (10) | 4,221,864 (16) | 4,850,493 (16) | 768,711 (7) | 4,776,635 (5) | 5,066,886 (3) | 4,878,852 (3) | 4,612,537 (16) | 1,560,879 (7) | 3,804,335 (8) | 5,240,116 (17) | 10.7/20 | 47,482,132 |
| sqx | 4,662,560 (7) | 2,078,866 (6) | 991,992 (12) | 4,105,933 (6) | 4,699,518 (11) | 878,469 (14) | 4,808,697 (15) | 5,102,452 (10) | 4,908,341 (14) | 4,590,245 (10) | 1,836,245 (9) | 4,415,575 (9) | 5,208,275 (10) | 9.8/20 | 48,287,168 |
| tgz | 4,707,481 (15) | 2,165,409 (16) | 907,006 (8) | 4,133,949 (12) | 4,684,949 (7) | 861,638 (11) | 4,807,701 (14) | 5,105,913 (14) | 4,909,789 (15) | 4,588,822 (9) | 1,853,650 (15) | 4,451,792 (17) | 5,202,392 (6) | 7.8/20 | 48,380,491 |
| uha | 4,498,275 (1) | 1,474,005 (1) | 136,880 (2) | 3,879,360 (2) | 4,625,014 (2) | 284,363 (2) | 4,760,572 (3) | 5,104,837 (11) | 4,879,047 (4) | 4,237,400 (1) | 1,233,812 (2) | 2,435,124 (2) | 5,187,408 (2) | 17.3/20 | 44,736,097 |
| yz1 | 4,814,935 (19) | 2,128,899 (8) | 924,706 (9) | 4,279,162 (19) | 4,686,669 (8) | 804,198 (9) | 4,810,966 (17) | 5,124,596 (18) | 4,922,886 (17) | 4,568,274 (7) | 1,901,300 (18) | 4,561,179 (19) | 5,207,874 (9) | 6.4/20 | 48,735,644 |
| zip | 4,701,064 (10) | 2,155,923 (11) | 1,009,814 (14) | 4,135,619 (13) | 5,270,565 (20) | 877,679 (13) | 4,799,508 (9) | 5,101,205 (9) | 4,898,961 (9) | 4,599,883 (12) | 1,839,080 (10) | 4,450,719 (13) | 5,264,564 (20) | 7.5/20 | 49,104,584 |
| Intermediate compressed size | 4,701,089 | 2,152,155 | 962,880 | 4,130,160 | 4,696,327 | 851,884 | 4,803,740 | 5,103,645 | 4,902,262 | 4,593,983 | 1,839,634 | 4,448,505 | 5,210,556 | 48,519,559 | |
| Intermediate compression rate | 10.6 % | 59.0 % | 81.7 % | 21.4 % | 10.5 % | 83.8 % | 8.4 % | 2.8 % | 6.8 % | 12.6 % | 65.0 % | 15.4 % | 1.0 % | 29.0 % |
Globally, the three best methods tested are rk, rar and 7z. WinRK and WinRar are commercial software, 7-zip is free, open source (LGPL licence) and can be used with Linux.
See also
Data compression topics
- Algorithmic complexity theory
- Information entropy
- Self-extracting archive
- Image compression
- Speech compression
- Video compression
- Multimedia compression
- Minimum description length
- Minimum message length (two-part lossless compression designed for inference)
- List of archive formats
- List of file archivers
- Comparison of file archivers
- List of Unix programs
- Free file format
- HTTP compression
- Reverse Delta
- Magic compression algorithm
Compression algorithms
Lossless data compression
- run-length encoding
- dictionary coders
- LZ77 & LZ78
- LZW
- Burrows-Wheeler transform
- prediction by partial matching (also known as PPM)
- context mixing
- Dynamic Markov Compression (DMC)
- entropy encoding
- Huffman coding (simple entropy coding; commonly used as the final stage of compression)
- Adaptive Huffman coding
- arithmetic coding (more advanced)
- Shannon-Fano coding
- range encoding (same as arithmetic coding, but looked at in a slightly different way)
- T-code, A variant of Huffman code
- Golomb coding (simple entropy coding for infinite input data with a geometric distribution)
- universal codes (entropy coding for infinite input data with an arbitrary distribution)
- Elias gamma coding
- Fibonacci coding
Lossy data compression
- discrete cosine transform
- fractal compression
- fractal transform
- wavelet compression
- vector quantization
- linear predictive coding
- Distributed Source Coding Using Syndromes, for correlated data
- Modulo-N code for correlated data
- A-law Compander
- Mu-law Compander
Example implementations
- DEFLATE (a combination of LZ77 and Huffman coding) – used by ZIP, gzip and PNG files
- LZMA used by 7-Zip and, to a lesser extent, StuffitX
- LZO (very fast LZ variation, speed oriented)
- LZX (an LZ77 family compression algorithm)
- Unix compress utility (the .Z file format), and GIF use LZW
- Unix pack utility (the .z file format) used Huffman coding
- bzip2 (a combination of the Burrows-Wheeler transform and Huffman coding)
- PAQ (very high compression based on context mixing, but extremely slow; competing in the top of the highest compression competitions)
- JPEG (image compression using a discrete cosine transform, then quantization, then Huffman coding)
- MPEG (audio and video compression standards family in wide use, using DCT and motion-compensated prediction for video)
- MP3 (a part of the MPEG-1 standard for sound and music compression, using subbanding and MDCT, perceptual modeling, quantization, and Huffman coding)
- AAC (part of the MPEG-2 and MPEG-4 audio coding specifications, using MDCT, perceptual modeling, quantization, and Huffman coding)
- Vorbis (DCT based AAC-alike audio codec, designed with a focus on avoiding patent encumbrance)
- JPEG 2000 (image compression using wavelets, then quantization, then entropy coding)
- TTA (uses linear predictive coding for lossless audio compression)
- FLAC (linear predictive coding for lossless audio compression)
Corpora
Data collections, commonly used for comparing compression algorithms.References
External links
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- Data Compression Benchmarks and Tests
- Data Compression Tutorial
- Compression Comparison Guide on various settings
- Large Data Compression Benchmarks and Tests
- Almost complete portraits of Data Compression inventors
- Data Compression - Systematisation by T.Strutz
- Lossless Data Compression by Greg Goebel
- How Stuff Works - File Compression
- Ultimate Command Line Compressors
- The Data Compression News Blog
- Practical Compressor Test (Compares speed and efficiency for commonly used compression programs)
- The Monthly Data Compression Newsletter
- Compressed File Types and File Extensions
- Image and Video Compression Learning Tool (VcDemo)
Data compression methods
| Lossless compression methods | ||||
|---|---|---|---|---|
| Audio compression methods |
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| Image compression methods |
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| Video compression |
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| Timeline of information theory, data compression, and error-correcting codes | ||||
source code (commonly just source or code) is any sequence of statements and/or declarations written in some human-readable computer programming language.
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Computer science, or computing science, is the study of the theoretical foundations of information and computation and their implementation and application in computer systems.
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Information theory is a branch of applied mathematics and engineering involving the quantification of information to find fundamental limits on compressing and reliably communicating data.
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BIT is an acronym for:
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- Bannari amman Institute of Technology
- Bangalore Institute of Technology
- Beijing Institute of Technology
- Benzisothiazolinone
- Bilateral Investment Treaty
- Bhilai Institute of Technology - Durg
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In communications, a code is a rule for converting a piece of information (for example, a letter, word, or phrase) into another form or representation, not necessarily of the same type.
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An encoder is a device used to change a signal (such as a bitstream) or data into a code. The code may serve any of a number of purposes such as compressing information for transmission or storage, encrypting or adding redundancies to the input code, or translating from one code
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ZIP
A zip archive icon from the Nuvola icon set.
File extension:
MIME type:
Uniform Type Identifier: com.pkware.
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A zip archive icon from the Nuvola icon set.
File extension:
.zipMIME type:
application/zipUniform Type Identifier: com.pkware.
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A file archiver is a computer program that combines a number of files together into one archive file, or a series of archive files, for easier transportation or storage. Many file archivers use lossless data compression in order to reduce the archive's size.
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A sender was a circuit in a 20th century electromechanical telephone exchange which sent telephone numbers and other information to another exchange. In some American exchange designs, for example 1XB switch the same term was also used to refer to the circuit that received this
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Information is the result of processing, gathering, manipulating and organizing data in a way that adds to the knowledge of the receiver. In other words, it is the context in which data is taken.
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Hard disk drive
An IBM hard disk drive with the metal cover removed. The platters are highly reflective.
Date Invented: September 13 1956
Invented By: An IBM team led by Reynold Johnson
Connects to:
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An IBM hard disk drive with the metal cover removed. The platters are highly reflective.
Date Invented: September 13 1956
Invented By: An IBM team led by Reynold Johnson
Connects to:
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Bandwidth is the difference between the upper and lower cutoff frequencies of, for example, a filter, a communication channel, or a signal spectrum, and is typically measured in hertz.
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lossy compression method is one where compressing data and then decompressing it retrieves data that may well be different from the original, but is close enough to be useful in some way.
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Lossless data compression is a class of data compression algorithms that allows the exact original data to be reconstructed from the compressed data. This can be contrasted to lossy data compression, which does not allow the exact original data to be reconstructed from the
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lossy compression method is one where compressing data and then decompressing it retrieves data that may well be different from the original, but is close enough to be useful in some way.
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Fidelity is a notion that at its most abstract level implies a truthful connection to a source or sources. Its original meaning dealt with loyalty and attentiveness to one's duty to a lord or a king, in a broader sense than the related concept of fealty.
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compression artifact (or artefact) is the result of an aggressive data compression scheme applied to an image, audio, or video that discards some data which is determined by an algorithm to be of lesser importance to the overall content but which is nonetheless discernible
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encryption is the process of transforming information (referred to as plaintext) to make it unreadable to anyone except those possessing special knowledge, usually referred to as a key.
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Run-length encoding (RLE) is a very simple form of data compression in which runs of data (that is, sequences in which the same data value occurs in many consecutive data elements) are stored as a single data value and count, rather than as the original run.
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In telecommunication, the term bandwidth compression has the following meanings:
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- The reduction of the bandwidth needed to transmit a given amount of data in a given time.
- The reduction of the time needed to transmit a given amount of data in a given bandwidth.
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as a college campus, industrial complex, or a military base. A CAN, may be considered a type of MAN (metropolitan area network), but is generally limited to an area that is smaller than a typical MAN.
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Executable compression is any means of compressing an executable file and combining the compressed data with the decompression code it needs into a single executable.
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Image compression is the application of Data compression on digital images. In effect, the objective is to reduce redundancy of the image data in order to be able to store or transmit data in an efficient form.
Image compression can be lossy or lossless.
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Image compression can be lossy or lossless.
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digital camera is an electronic device used to capture and store photographs digitally, instead of using photographic film like conventional cameras, or recording images in an analog format to magnetic tape like many video cameras.
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DVD
Media type: Optical disc
Capacity: 4.7 GB (single layer), 8.5 GB (dual layer)
Usage: Data storage, audio, video, games
Optical disc authoring
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Media type: Optical disc
Capacity: 4.7 GB (single layer), 8.5 GB (dual layer)
Usage: Data storage, audio, video, games
Optical disc authoring
- Optical disc
- Optical disc image
- Recorder hardware
- Authoring software
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MPEG-2 is a standard for "the generic coding of moving pictures and associated audio information".[1] It describes a combination of lossy video compression and lossy audio compression (audio data compression) methods which permit storage and transmission of movies using
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A video codec is a device or software that enables video compression and or decompression for digital video. The compression usually employs lossy data compression. Historically, video was stored as an analog signal on magnetic tape.
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Video compression refers to reducing the quantity of data used to represent video images, and this is almost always coupled with the goal of retaining as much of the original's quality as possible.
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Audio compression can mean two things:
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- Audio data compression - in which the amount of data in a recorded waveform is reduced for transmission. This is used in CD and MP3 encoding, internet radio, and the like.
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Psychoacoustics is the study of subjective human perception of sounds. Alternatively it can be described as the study of the psychological correlates of the physical parameters of acoustics.
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Herod_Archelaus
