Neural network processing to minimize quantization losses

Yu-Jhih Wu; Paul M. Chau

doi:10.1117/12.160462

1 November 1993 Neural network processing to minimize quantization losses

Yu-Jhih Wu, Paul M. Chau

Proceedings Volume 2027, Advanced Signal Processing Algorithms, Architectures, and Implementations IV; (1993) https://doi.org/10.1117/12.160462
Event: SPIE's 1993 International Symposium on Optics, Imaging, and Instrumentation, 1993, San Diego, CA, United States

Abstract

A general neural network co-processor has been investigated and designed to adaptively adjust the quantization thresholds of a data quantizer, and thus a data quantizer with minimum quantization loss can be obtained. For a given probability density function and number of quantization levels, the neural network is designed to learn the near optimal quantization uniform step-size which minimizes the loss caused by the quantizer. With this neural network co-processor approach, consistent and substantial performance improvements have been verified on either an AWGN or a Rayleigh fading communication channel with convolutional encoder and maximum likelihood decoder. This general neural network co-processor approach can be applied to any digital signal processing system which has quantization loss, such as digital communication, image data compression, or adaptive signal processing.

Citation Download Citation

Yu-Jhih Wu and Paul M. Chau "Neural network processing to minimize quantization losses", Proc. SPIE 2027, Advanced Signal Processing Algorithms, Architectures, and Implementations IV, (1 November 1993); https://doi.org/10.1117/12.160462

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