A general framework for the second-level adaptive prediction

We present a study of a general framework for second-level adaptive prediction which is formed from a group of predictors. It is a natural extension to that of the first-level which is formed directly from a group of pixels. The proposed framework offers a greater degree of freedom for adaptation and addresses some of the tough problems such as model uncertainty that is inherent to the first-level prediction methods. We show that the proposed methods of taking weighted average (WAVE) and weighted median (WMED) of a group of predictions are alternative and competitive adaptive image prediction methods. We have achieved better compression performance than that of TMW^Lego by combining a group of linear predictors.