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Data Compression ○｜Definition｜1st｜20261017135122-00-⌔
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.
Data compression algorithms present a space–time complexity trade-off between the bytes needed to store or transmit information, and the computational resources needed to perform the encoding and decoding. The design of data compression schemes involves balancing the degree of compression, the amount of distortion introduced (when using lossy data compression), and the computational resources or time required to compress and decompress the data.5
Printed 2026-10-17.
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Link to original Footnotes
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. ↩
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: Proceedings of the First International Conference on Contours of Computing Technology. Berlin: Springer. pp. 275–283. doi:10.1007/978-81-8489-989-4_51. ISBN 978-81-8489-988-7. ↩
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