04-05-2017, 01:17 PM
In signal processing, data compression, source coding or bit rate reduction involves encoding information using fewer bits than the original representation. Compression may be loss or loss. Lossless compression reduces bits by identifying and eliminating statistical redundancy. No information is lost on lossless compression. Lossy compression reduces bits by eliminating unnecessary or less important information. The process of reducing the size of a data file is called data compression. In the context of data transmission, it is called source coding (coding performed on the data source before being stored or transmitted) as opposed to channel coding.
Compression is useful because it reduces the resources needed to store and transmit data. The computational resources are consumed in the compression process and, usually, in the inversion of the process (decompression). Data compression is subject to a compromise of spatiotemporal complexity. For example, a video compression scheme may require expensive hardware to decompress the video fast enough to be seen as it is being decompressed, and the option to decompress the entire video before viewing it may be inconvenient or require additional storage . The design of data compression schemes involves compromises between several 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.
