entropiakoodausmenetelmiä

Entropiakoodaus, also known as entropy coding, is a type of lossless data compression method. It works by assigning shorter codes to more frequent symbols and longer codes to less frequent symbols. The fundamental principle behind entropy coding is derived from information theory, specifically Shannon's source coding theorem. This theorem states that the minimum average code length for a source is bounded by its entropy.

There are several common techniques within entropy coding. Huffman coding is one of the earliest and most widely used. It constructs a prefix code tree based on the probabilities of symbols, ensuring that no codeword is a prefix of another, which allows for unambiguous decoding. Another prominent method is arithmetic coding. Instead of assigning a specific bit string to each symbol, arithmetic coding represents the entire message as a single fraction within the interval [0, 1). As more symbols are processed, the interval narrows, and the final fraction uniquely identifies the sequence.

The effectiveness of entropy coding is directly related to the predictability of the data. If a data source generates symbols with highly uneven probabilities (i.e., high entropy), entropy coding can achieve significant compression. Conversely, for data with uniformly distributed symbol probabilities (low entropy), the compression ratio will be minimal. Entropiakoodaus is often used as a final stage in many compression algorithms, following other techniques like run-length encoding or dictionary-based methods, which transform the data into a more statistically predictable form suitable for entropy coding.