Statistical Mechanical Approach to Lossy Data Compression:Theory and Practice
<|reference_start|>Statistical Mechanical Approach to Lossy Data Compression:Theory and Practice: 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.<|reference_end|>
@article{hosaka2005statistical,
    title={Statistical Mechanical Approach to Lossy Data Compression:Theory and
  Practice},
    author={Tadaaki Hosaka, Yoshiyuki Kabashima},
    journal={arXiv preprint arXiv:cs/0509086},
    year={2005},
    doi={10.1016/j.physa.2006.01.013},
    archivePrefix={arXiv},
    eprint={cs/0509086},
    primaryClass={cs.IT math.IT}
}