Stephen D. Voran

Abstract: We investigate the use of an adaptive processor (a quantizer pseudoinverse) and the statistics of the associated pseudoerror signal to reduce quantization error in scalar quantizers when a small amount of prior knowledge about the signal x is available. This approach uses both the quantizer representation points and the thresholds at the receiver. No increase in the transmitted data rate is required. We discuss examples that use low-pass, high-pass, and band-pass signals along with an adaptive processor that consists of a set of filters and clippers. Matching a single pseudoerror statistic to a target value is sufficient to attain modest reductions in quantization error in situations with one degree of freedom. Adaptive processing based on a pair of pseudoerror statistics allows for quantization noise reduction in problems with two degrees of freedom.

Keywords: signal processing; adaptive filtering; information retrieval; quantization; band pass filters; signal resolution; error analysis; noise reduction; noise shaping; signal design

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