Coding bounded support data with beta distribution

The probability density function (PDF) optimized quantization has been shown to be more efficient than the conventional quantization methods. In practical application, the data with bounded support can be modelled better with bounded support distribution (e.g. beta distribution, Dirichlet distribution) and a better quantization performance could be achieved by a more reasonable modelling. In this paper, we study the distortion rate (D-R) performance and the high rate quantization performance of the beta distribution. To implement a quantizer efficiently, a practical quantization scheme is proposed. The proposed scheme takes the advantages of conventional compander and exhaustive training. The advantage of the proposed scheme is verified with both theoretical experiment and practical application.