Quantization denotes the heart of analog to digital (AID) conversion, the process of approximating a continuous range of values by a set of discrete symbols (bits). Scalar quantizers are primarily used for AID conversion, while vector quantizers are used for sophisticated digital signal processing. Signal compression is a procedure of digitalization of continuous signals, by using as few bits as possible, while truing at the same time to maintain a reasonable level of quality. The level of quality is usually measured by mean-square-error (quantization noise) or signal-to-quantization noise ratio (SQNR) [1]. In a number of papers the quantization of Gaussian source was analyzed since the probability density function (PDF) of the instantaneous speech signal values for lower number of digitalization samples is better represented by Gaussian then the Laplacean function [1]. The volume of speech signal also has Gaussian distribution. Finally, for appropriate design of filters, every continuous signal that is filtered, will have an approximately Gaussian distribution. Memoryless Gaussian source is commonly used and is important in many areas of telecommunications and computer science. The analysis of overcoming deficiencies and limitations of the existing signal compandors, when the input speech signal is modelled by the Gaussian distribution, was presented previously in [2]. Optimization of robust and switched nonuniform scalar quantization model is analyzed in [3], for the case when the power of an input signal varies in a wide range. In this paper, we have analyzed A-law characteristic for swiched non-uniform scalar quantization of Gaussian source. An A-law algorithm is a standard companding algorithm, used in European digital communications systems to optimize, i.e., modify, the dynamic range of an analog signal for digitizing.
展开▼
