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In this paper, two forward adaptive piecewise uniform scalar quantizers are  proposed for high-quality quantization of speech signals modeled by the  Laplacian probability density function. In designing both forward adaptive  piecewise uniform scalar quantizers an equidistant support region partition  is assumed and a distribution of the number of reproduction levels per  segments is optimized. The proposed models differ in the approach of  determining the reproduction levels. In particular, one model defines the  reproduction levels as the cell centroids and the other one as the cell  midpoints. We show that, in the high-resolution case, the proposed quantizers  provide approximately the same performance being close to the one of the  forward adaptive nonlinear scalar compandor with equal number of quantization  levels.

Two approaches to design of forward adaptive piecewise uniform scalar quantizers