Optimize neural network based in-loop filters through iterative training

The latest video coding standard, named Versatile Video Coding (VVC), had been finalized in 2020. In our previous work, several neural network based in-loop filters are proposed to improve the compression performance beyond VVC. However, the effect of inter-frame referencing mechanism is not taken into the consideration, which leads to an inconsistency between the training process and the final testing process. To solve this problem, an iterative training method is proposed in this paper to further optimize the neural network based in-loop filters. Based on the proposed method, up to 1.74% additional YUV BD-rate saving can be achieved. Compared with VVC, the experiments show that on average 14.00% YUV BD-rate saving is achieved by the filter with 22 models while 11.21% YUV BD-rate saving is achieved by the filter with one single model. Moreover, the subjective evaluation has verified that the performance of the single model filter is better than VVC by a noticeable margin.