3. Adaptive Rounding Encoding Technique using an Equal Expected-Value Rule
As discussed above, if the input p.d.f. is Laplacian distributed and if we can estimate λ, then the optimal f can be found analytically. But, usually the estimate of input p.d.f. is not available, then, how to select the rounding offset f?
In order to select rounding offset f adaptively, an adaptive quantization encoding technique using an equal expected-value rule is proposed by Gary Sullivan from Microsoft. The adaptive adjustment of the rounding offset f occurs only in the encoding quantization process, which tries to select f without using any priori model knowledge on the input W. The aim is to make that the mean of the absolute value of the input, |W|, is equal to its expected reconstruction value |W’|, i.e.,
                                                                                         (5)
Any values in an interval would be reconstructed to some W’, so the distribution of W’ is a probability ma[......]
Permanent Link: Quantization techniques in JM/KTA – Part 3