Multiple Feature-based Classifications Adaptive Loop Filter

Abstract

In video coding, adaptive loop filter (ALF) has attracted attention due to its increasing coding performances. Recently ALF has been further developed for its extension, which introduces geometry transformation-based adaptive loop filter (GALF) outperforming the existing ALF techniques. The main idea of ALF is to apply a classification to obtain multiple classes, which gives a partition of a set of all pixel locations. After that, a Wiener filter is applied for each class. Therefore, the performance of ALF essentially relies on how its classification behaves. In this paper, we introduce a novel classification method, Multiple feature-based Classifications ALF (MCALF) extending a classification in GALF and show that it increases coding efficiency while only marginally raising encoding complexity. The key idea is to apply more than one classifier at the encoder to group all reconstructed samples and then to select a classifier with the best RD-performance to carry out the classification process. Simulation results show that around 2% bit rate reduction can be achieved on top of GALF for some selected test sequences.