Coding Algorithm Based on the Simplified Semilogarithmic Compression Law for Discrete Input Samples with Laplacian Distribution

Abstract

In this paper, a novel simple algorithm based on the simplified semilogarithmic quantizer is proposed for Laplacian source coding. An analysis of the signal to quantization noise ratio (SQNR) of the simplified semilogarithmic quantizer is provided in the wide variance range for the cases of discrete input samples and continual input samples with Laplacian distribution. For the assumed discrete input samples and continual input samples it is shown that the higher SQNR is achieved by the proposed semilogarithmic quantizer when compared to the uniform quantizer. Another major contribution of this paper is the proof that in the area of smaller variance values the simplified semilogarithmic quantizer provides significantly higher SQNR values than the uniform quantizer, which is of great importance, especially in image coding, where the range of smaller variances is more probable than the range of higher variance values. Due to the simple realization structure and higher achieved SQNR than the uniform quantizer, one can expect that the proposed algorithm will be very applicable in coding of signals which, as well as speech signals and signals of difference between adjacent pixel values of image, follow Laplacian distribution.

DOI: http://dx.doi.org/10.5755/j01.eee.20.3.6679