Statistical Mechanical Approach to Lossy Data Compression:Theory and Practice

Details Statistical Mechanical Approach to Lossy Data Compression:Theory and Practice Statistical Mechanical Approach to Lossy Data Compression:Theory and Practice

2005-09-28

arxiv.org/abs/cs/0509086v1

Computer Science

Information Theory

10 pages, 2 figures, REVTEX preprint

Scientific paper

10.1016/j.physa.2006.01.013

The encoder and decoder for lossy data compression of binary memoryless sources are developed on the basis of a specific-type nonmonotonic perceptron. Statistical mechanical analysis indicates that the potential ability of the perceptron-based code saturates the theoretically achievable limit in most cases although exactly performing the compression is computationally difficult. To resolve this difficulty, we provide a computationally tractable approximation algorithm using belief propagation (BP), which is a current standard algorithm of probabilistic inference. Introducing several approximations and heuristics, the BP-based algorithm exhibits performance that is close to the achievable limit in a practical time scale in optimal cases.

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