A lossless image prediction algorithm using slope estimation and least square optimization

In this paper we present two computationally simple algorithms that can be used for prediction of pixels of images. In one of the algorithms, prediction is made by estimating intensity value variations in four directions and their reciprocals are used to make prediction of unknown pixel. This algorithm captures local characteristics of the unknown pixel well as it uses only a small number of neighborhood pixels. The other algorithm finds slope as the relative intensity-value variations and classifies image pixels in fourteen bins by classifying the slope in the same number of bins. LS based predictors are estimated for pixels belonging to each of the bins and hence the they represent global characteristics of these pixels. Since one algorithm takes care of local characteristics while the other one represents global feature, we propose a switching method for these two algorithms that takes advance of both the algorithms. Switching is done on a pixel-by-pixel basis and the same gives approximately 0.10 bpp better performance as compared to some of the computationally complex methods reported in literature at a lower computational complexity.