In computer science and information theory, data compression, source coding, or bit-rate reduction involves encoding information using fewer bits
than the original representation. Compression can be 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 identifying marginally important information and
removing it.
Compression is useful because it helps reduce the consumption of resources such as data space or transmission capacity.
Because compressed data must be decompressed to be used, this extra
processing imposes computational or other costs through decompression.
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.
The design of data compression schemes involve trade-offs among various
factors, including the degree of compression, the amount of distortion
introduced (e.g., when using lossy data compression), and the computational resources required to compress and uncompress the data.
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