Compressing

Compressing is the process of encoding information using fewer bits than the original representation. The goal is to reduce the amount of data required to store or transmit a set of information. When the method is lossless, the original data can be perfectly reconstructed from the compressed form; when the method is lossy, some information is discarded to achieve higher compression, and exact reconstruction is not possible.

Data compression can be applied to any data type, including text, images, audio, and video. It is typically categorized as lossless or lossy. Lossless techniques preserve all information and are common for software, documents, and source code. Lossy techniques sacrifice some detail to achieve higher ratios and are used for multimedia where human perception matters.

Common lossless algorithms include Huffman coding, arithmetic coding, and dictionary-based methods such as LZ77, LZ78, and LZW. Modern lossless formats include Deflate (ZIP, gzip) for general data and image formats like PNG that rely on deflate; audio formats such as FLAC use dedicated lossless codecs. Lossy techniques rely on perceptual models and transform coding; JPEG uses discrete cosine transform and quantization, while MP3 and AAC apply psychoacoustic compression to audio, and most video codecs use transform coding, quantization, and motion estimation.

Processing steps typically involve modeling redundancy, encoding with fewer bits (entropy coding), and optional preprocessing such as filtering or block transforms. Decompression reverses these steps to recover the original data (lossless) or approximate it (lossy).

Performance trade-offs include compression ratio, speed, memory usage, and quality. In lossy methods, artifacts may arise; in any method, error resilience and accessibility considerations influence codec choice. The field rests on information theory and has shaped digital storage and communication since the mid-20th century.