Quantization Noise in ADPCM Systems

The system considered here consists of a differential pulse-code modulator (DPCM) in which the quantizer is replaced by an adaptive quantizer. Adaptation is accomplished by adjusting the stepsize at every sampling instant, depending upon the magnitude of the quantization error. Quantizing noise in adaptive DPCM (ADPCM) systems falls into three categories: granularity, slope overload, and quantizer saturation. Granular noise occurs because only a finite number of levels are available to represent the analog input signal during the encoding process. Slope overload happens when the slope of the input signal increases faster than the adaptive system can follow. Quantizer saturation exists because nonoptimal decisions on step-size adjustments can lead to situations in which quantizer overload occurs without slope overload. The result is an error larger than a purely granular noise analysis would suggest. Equations for the three types of quantizing noise in ADPCM systems are derived, and computer simulations are perforated. For flatand RC-filtered Gaussian input signals, oversampled at various rates, the simulation results agree well with theoretical predictions. Comparisons indicate that the ADPCM can perform better than the best analogous nonadaptive system in terms of signal-to-quantizing-noise ratio. Furthermore, the optimal operating point for the ADPCM is much less sensitive to changes in input signal parameters and system component values than in nonadaptive systems.