Generalised locally adaptive DPCM

Summary form only given. In differential pulse code modulation (DPCM) we make a prediction fˆ=Σa(i)-f(i) of the next pixel using a linear combination of neighbouring pixels f(i). It is possible to have the coefficients a(i)s constant over a whole image, but better results can be obtained by adapting the a(i)s to the local image behaviour as the image is encoded. One difficulty with present schemes is that they can only produce predictors with positive a(i)s. This is desirable in the presence of noise, but in regions where the intensity varies smoothly, we require at least one negative coefficient to properly estimate a gradient. However, if we consider the four neighbouring pixels as four local sub-predictors W, N, NW and NE, and the gradient measure as the sum of absolute prediction errors of those sub-predictors within the local neighbourhood, then we can use any sub-predictors we choose, even nonlinear ones. In our experiments, we chose to use three additional linear predictors suited for smooth regions, each having one negative coefficient. Results were computed for three versions of the standard JPEG test set and some 12 bpp medical images