Simplified CNN In-Loop Filter with fixed Classifications

In this paper, we present a novel in-loop filter for video coding which is based on a convolutional neural network (CNN). For that, the adaptive loop filter (ALF) of Versatile Video Coding (VVC) is generalized to define the model architecture for a CNN-based in-loop filter which requires significantly lower computational complexity compared to other existing CNN-based in-loop filters. Experimental results show that, under the JVET common test conditions, BD-rate savings of 1.84% and 1.83% can be achieved compared to VVC for the all-intra and random-access configurations, respectively. At the same time, the computational complexity is at only about 455 multiplications per luma sample.