Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
410889 | Neurocomputing | 2006 | 9 Pages |
Abstract
A novel algorithm for jointly optimizing source and channel codes is presented in this paper. The algorithm uses the channel-optimized vector quantization (COVQ) for the source code, and rate-punctured convolutional coding (RCPC) for the channel code. The genetic algorithm (GA) is used for the concurrent design of both source and channel codes. The GA enhances the robustness of the rate–distortion performance of the COVQ to the selection of initial codewords. In addition, it reduces the computational time for realizing the unequal error protection scheme best matched to the COVQ. Numerical results show that the algorithm attains near optimal performance while having low computational complexity.
Keywords
Related Topics
Physical Sciences and Engineering
Computer Science
Artificial Intelligence
Authors
Chien-Min Ou, Wen-Jyi Hwang, Wen-Wei Hu, Tsung-Yan Lo,