Data compression

      Data compression is the art of reducing the number of bits needed to store or transmit data. Compression can be either lossless or lossy. Losslessly compressed data can be decompressed to exactly its original value or in lossy compressed data can not be decompressed to into exact value, actually some of the data can be lossed.

All data compression algorithms consist of at least a model and a coder (with optional preprocessing transforms). A model estimates the probability distribution (E is more common than Z). The coder assigns shorter codes to the more likely symbols. There are efficient and optimal solutions to the coding problem. However, optimal modeling has been proven not computable. Modeling (or equivalently, prediction) is both an artificial intelligence (AI) problem and an art.
Lossy compression consists of a transform to separate important from unimportant data, followed by lossless compression of the important part and discarding the rest. The transform is an AI problem because it requires understanding what the human brain can and cannot perceive.

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