Performance-Complexity Analysis of Adaptive Loop Filter with a CNN-based Classification

Recently, convolutional neural network (CNN)-based in-loop filters have been introduced for video coding and they show huge coding gains. However, one of the main issues of this approach is the high computational complexity of these filters. In this paper, we present various settings for CNN-based in-loop filters targeting on the reduction of their decoder complexity and describe the corresponding gain-complexity trade-offs. For this, we introduce an effective complexity measure and show that it is possible to notably reduce this value for some CNN-based in-loop filters while keeping the compression gain over Versatile Video Coding (VVC).