Adaptive Loop Filter with a CNN-Based Classification

In this paper, a data-driven generalization of the adaptive loop filter (ALF) of Versatile Video Coding (VVC) is presented. It is shown how the conventional ALF process of classification and FIR filtering can be generalized to define a natural model architecture for convolutional neural network (CNN) based in-loop filters. Experimental results show that over VVC, average bit-rate savings of 3.85%/4.75% and 4.39%/4.33% can be achieved for the all intra and random access configurations in the low- and high-QP settings.