Entropycoded

Entropycoded is a term used in information theory and data compression to describe data that has been encoded using entropy coding techniques. The word combines entropy, a measure of the unpredictability of a source, with coding, the process of mapping symbols to binary sequences. Entropycoding aims to minimize the expected code length of a data stream by assigning shorter codes to more probable symbols and longer codes to less probable ones, so that the average length approaches the source entropy H(X).

The method relies on a probabilistic model of the source. Common entropy coding techniques include Huffman coding, which assigns prefix-free codes based on symbol frequencies, and arithmetic or range coding, which encode in a single fractional bit stream. Modern data compressors often combine entropy coding with context modeling and adaptive probability estimation to maintain efficiency across varying data.

Applications include lossless compression of text, images, and audio, as well as the entropy coding stage in many multimedia codecs such as JPEG, PNG, MP3, and AAC. The quality and speed of entropycoding depend on model accuracy, implementation details, and the handling of symbol probability changes.

Limitations include the need for accurate probability models and synchronization between encoder and decoder. The technique also incurs overhead for code tables or state information and can be sensitive to errors that disrupt subsequent decoding.

Historically, entropy coding builds on Claude Shannon's foundational work in information theory, with practical algorithms such as Huffman coding in the 1950s and arithmetic/range coding developing later. The term entropycoded is used informally to describe data produced by these techniques.