Scalar Quantization With Optimized Receiver-Sided Adaptive Codebook Reconstruction Levels Controlled by a Predictor

The well known scalar non-uniform Lloyd-Max quantizer (LMQ) is optimized for the minimum mean squared error (MMSE). Its quantization codebook reconstruction levels are determined by the centroid of the signal probability density function (PDF) in a given quantization interval. Redundancy from correlated source processes is not used at all. To improve scalar quantizer performance for correlated processes, a novel decoding approach is proposed in this paper, employing a receiver-sided optimized predictor along with a standard LMQ encoder. Based on the prediction error PDF, the LMQ quantization codebook reconstruction levels are adapted over time. Moreover, a full search for the optimal values of the predictor coefficient and the variance of the prediction error is carried out. Compared to the standard LMQ, our new approach achieves a signal-to-noise ratio (SNR) gain of about 1 dB both in error-free and error-prone transmission conditions.