Progressively adaptive scalar quantization
P.W. Wong · 2002
We consider a progressively adaptive scalar quantization scheme where the quantizer is adjusted as input samples are processed. The quantizer adjustments are based on an estimated probability density function (pdf) consisting of piecewise polynomials. This pdf is calculated from the bin probabilities that are estimated from quantized samples. The effect of the support of the pdf on the performance of Lloyd-Max quantizers is also examined. Experimental results on the progressively adaptive quantizer for non-stationary sources are shown.