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The Lempel–Ziv (LZ) compression methods are among the most popular algorithms for lossless storage. This is a basic example of run-length encoding there are many schemes to reduce file size by eliminating redundancy. ' the data may be encoded as '279 red pixels'. For example, an image may have areas of color that do not change over several pixels instead of coding 'red pixel, red pixel. Lossless compression is possible because most real-world data exhibits statistical redundancy. Lossless data compression algorithms usually exploit statistical redundancy to represent data without losing any information, so that the process is reversible. The design of data compression schemes involves trade-offs among various factors, including the degree of compression, the amount of distortion introduced (when using lossy data compression), and the computational resources required to compress and decompress the data

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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. Data compression is subject to a space–time complexity trade-off. Computational resources are consumed in the compression and decompression processes. 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.Ĭompression is useful because it reduces the resources required to store and transmit data. 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. The process of reducing the size of a data file is often referred to as data compression. 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. Lossy compression reduces bits by removing unnecessary or less important information. No information is lost in lossless compression. Lossless compression reduces bits by identifying and eliminating statistical redundancy. Any particular compression is either lossy or lossless. In information theory, data compression, source coding, or bit-rate reduction is the process of encoding information using fewer bits than the original representation. For the term in computer programming, see Source code.

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