Classified quadtree-based adaptive loop filter

Abstract

In this paper, we propose a classified quadtree-based adaptive loop filter (CQALF) in video coding. Pixels in a picture are classified into two categories by considering the impact of the deblocking filter, the pixels that are modified and the pixels that are not modified by the deblocking filter. A wiener filter is carefully designed for each category and the filter coefficients are transmitted to decoder. For the pixels that are modified by the deblocking filter, the filter is estimated at encoder by minimizing the mean square error between the original input frame and a combined frame which is a weighted average of the reconstructed frames before and after the deblocking filter. For pixels that the deblocking filter does not modify, the filter is estimated by minimizing the mean square error between the original frame and the reconstructed frame. The proposed algorithm is implemented on top of KTA software and compatible with the quadtree-based adaptive loop filter. Compared with kta2.6r1 anchor, the proposed CQALF achieves 10.05%, 7.55%, and 6.19% BD bitrate reduction in average for intra only, IPPP, and HB coding structures respectively.