In this paper, robust and switched nonuniform scalar quantization model is analyzed for the case when the power of an input signal varies in a wide range. This model of scalar quantization is used in order to give higher quality by increasing signal-to-quantization noise ratio (SNRQ) in a wide range of signal volumes (variances) with respect to its necessary robustness over a broad range of input variances. We observed μ-low compandor implementation to achieve compromise between high-rate digitalization and variance adaptation. Accurate estimate of the input signal variance is needed when finding the best compressor function for a compandor implementation. It enables quantizers to be adapted to the maximal amplitudes of input signals. In addition, we found the expression for distortion, which we used to estimate the suggested model. In the nature of optimizing parameters of this model, we derived conclusions about the possibilities of this switched quantization application in speech processing. We analized influence of codebook size and number on quality of transmission, and compared ITU-T G711 standard with our model. The main contribution of this model is increasing of quality and the possibility of his applying for digitalization of continuous signals in wide range.
- μ-low compounding
- Gaussian source
- Robust and switched quantization
- Variance adaptation
ASJC Scopus subject areas
- Signal Processing
- Control and Systems Engineering