Work

Quantization of Binary-Input Discrete Memoryless Channels

Year: 2014

Type: article

Abstract: The quantization of the output of a binary-input discrete memoryless channel to a smaller number of levels is considered. An algorithm, which finds an optimal quantizer, in the sense of maximizing mutual information between the channel input and quantizer output is given. This result holds for arbitrary channels, in contrast to previous results for restricted channels or a restricted number of quantizer outputs. In the worst case, the algorithm complexity is cubic M3in the number of channel outputs M. Optimality is proved using the theorem of Burshtein, Della Pietra, Kanevsky, and Nádas for mappings, which minimize average impurity for classification and regression trees.

Source: IEEE Transactions on Information Theory

DOI: 10.1109/tit.2014.2327016

Authors: Brian M. Kurkoski, Hideki Yagi

Institutions: Japan Advanced Institute of Science and Technology, University of Electro-Communications

Language: English

Topic: Advanced Data Compression Techniques

Subfield: Computer Vision and Pattern Recognition

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

Funder: Japan Society for the Promotion of Science

Awards: Signal Processing and Coding Methods for High-Density Flash Memory Systems, Refinement of Multi-User Information Theory for High-Speed Communication Network