Improved Linearization of the Optimal Compression Function for Laplacian Source

Abstract

In this paper, linearization of the optimal compression function is done and hierarchical coding (by coding the regions firstly and then the cells inside the region) is applied, achieving simple and fast process of coding and decoding. The signal at the entrance of the scalar quantizer is modeled by Laplacian probability density function. It is shown that the linearization of inner regions very little influences distortion and therefore only the last region should be optimized. Two methods of optimization of the last region are proposed, that improve performances of the scalar quantizer, and obtained SQNR (signal-to-quantization noise ratio) is close to that of the nonlinear optimal compression function.

Dual-mode quasi-logarithmic quantizer with embedded G.711 codec

“…In case of the Laplacian PDF given with (7), granular and overload distortion of the G.711 quantizer can be expressed in the form [1] D G.711…”

“…For example, in ITU-T G.711 standard [6] the piecewise linear approximation to µ-law or A-law logarithmic characteristic is implemented. The linearization of optimal compression function has been reported in [7].…”