ROBUST QUANTIZATION OF MEMORYLESS SOURCES USING DISPERSIVE FIR FILTERS

A novel approach to quantizing discrete-time memoryless sources is presented. An important feature is that its performance is largely insensitive to errors in modeling the input pdf. The method involves changing the amplitude distribution of the source to be approximately Gaussian by all-pass filtering, then applying a Lloyd-Max quantizer designed for a Gaussian source. After quantization, the samples are passed through another all-pass filter, which is an approximate inverse of the first filter. The mean-square error (MSE) for the overall process is roughly equal to the quantization MSE for the intermediate Gaussian signal, independent of the source statistics. For some sources, this is actually an improvement over direct, correct-model Lloyd-Max quantization. The cost of this technique is some delay due to filtering.