Entropykódolási

Entropykódolási refers to a class of data compression techniques that exploit the statistical properties of information sources. The fundamental principle behind entropy coding is that data with predictable patterns can be represented more efficiently than data with random patterns. It aims to assign shorter codes to more frequent symbols and longer codes to less frequent symbols, thereby minimizing the average code length.

The theoretical limit for lossless data compression is dictated by the entropy of the information source, a concept pioneered by Claude Shannon. Entropy, in this context, measures the average amount of information or uncertainty per symbol. Entropy coding methods strive to approach this theoretical limit.

Common types of entropy coding algorithms include Huffman coding and arithmetic coding. Huffman coding constructs a prefix code where no codeword is a prefix of another, ensuring unambiguous decoding. Arithmetic coding represents an entire message as a single fraction within the range [0, 1), with the length of the fraction inversely proportional to the probability of the message.

These techniques are widely used in various data compression standards, such as JPEG for images, MP3 for audio, and ZIP for general-purpose file compression. By reducing the number of bits required to represent data without losing information, entropy coding plays a crucial role in efficient storage and transmission of digital content.