An overview and performance evaluation of classification-based least squares trained filters.

Abstract

An overview of the classification-based least squares trained filters on picture quality improvement algorithms is presented. For each algorithm, the training process is unique and individually selected classification methods are proposed. Objective evaluation is carried out to single out the optimal classification method for each application. To optimize combined video processing algorithms, integrated solutions are benchmarked against cascaded filters. The results show that the performance of integrated designs is superior to that of cascaded filters when the combined applications have conflicting demands in the frequency spectrum.