Quantization Error in Predictive Coders

Abstract

Predictive coders have been suggested for use as analog data compression devices. Exact expressions for reconstructed signal error have been rare in the literature. In fact most results reported in the literature are based on the assumption of Gaussian statistics for prediction error. Predictive coding of first-order Gaussian Markov sequences are considered in this paper. A numerical iteration technique is used to solve for the prediction error statistics expressed as an infinite series in terms of Hermite polynomials. Several interesting properties of predictive coding are thereby demonstrated. First, prediction error is in fact close to Gaussian, even for the binary quantizer. Sencond, quantizer levels may be optimized at each iteration according to the calculated density. Finally, the existence of correlation between successive quantizer outputs is shown. Using the series solutions described above, performance in terms of meansquare reconstruction error versus bit rate can be shown to parallel the theoretical rate distortion function for the first-order Markov process by about 0.6 bits/sample at low bit rates.