Data Compression
📜 Abstract
This paper surveys a variety of data compression methods spanning almost 40 years of research, from the work of Shannon, Fano, and Huffman in the late 1940s to a technique developed in 1986. The aim of data compression is to reduce redundancy in stored or communicated data, thus increasing effective data density. Data compression has important application in the areas of file storage and distributed systems. Concepts from information theory as they relate to the goals and evaluation of data compression methods are discussed briefly. A framework for evaluation and comparison of methods is constructed and applied to the algorithms presented. Comparisons of both theoretical and empirical natures are reported, and possibilities for future research are suggested.
✨ Summary
The paper is a broad survey of lossless data-compression methods, covering information-theoretic foundations, fixed- and variable-length codes, Huffman and adaptive Huffman coding, arithmetic coding, dictionary-based methods including Lempel–Ziv techniques, and locally adaptive schemes. It distinguishes general-purpose methods from semantic-dependent approaches and compares algorithms using both theoretical properties and empirical performance. The paper’s main contribution is synthesis: it organizes several decades of compression research within a common framework for evaluating compression ratio, redundancy, computational cost, and adaptability.
Subsequent literature continues to cite the survey as a general reference when introducing or comparing compression methods. For example, later work on general-purpose compression for information retrieval cites it alongside foundational compression literature, and comparative studies of text-compression algorithms use it as background authority. (onlinelibrary.wiley.com) The paper has also been presented in industry-oriented technical reading as a foundational overview of compression research and its continuing relevance to storage and communication systems. (allthingsdistributed.com) No evidence was found in the reviewed sources that the survey itself introduced a production compression algorithm; its documented impact is primarily pedagogical and bibliographic, providing a structured reference point for subsequent research and engineering discussions. (dblp.org)