Entropy Encoding
Entropy encoding is a lossless data compression technique that assigns shorter codes to more frequent symbols and longer codes to less frequent symbols, based on information theory principles. It aims to reduce redundancy in data by encoding symbols according to their probability of occurrence, with the goal of achieving compression close to the theoretical entropy limit. Common algorithms include Huffman coding and arithmetic coding, widely used in file compression, multimedia codecs, and communication systems.
Developers should learn entropy encoding when working on data compression, storage optimization, or bandwidth-efficient transmission, such as in image/audio/video codecs (e.g., JPEG, MP3, H.264), file archivers (e.g., ZIP, GZIP), or network protocols. It is essential for reducing data size without loss of information, improving performance in resource-constrained environments like embedded systems or mobile applications, and is foundational for understanding information theory in computer science.