Improved method of deblocking filter based on convolutional neural network in VVC

With the rapid development of 5G and the Internet of Things, video has gained broad application space as a carrier of information, and video codec technology can reduce video storage space, save transmission bandwidth, and provide the possibility for the promotion of video applications. Versatile Video Coding (VVC), the latest generation of video coding standard today, still uses block-based coding, which inevitably introduces coding artifacts, and deblocking filters can effectively reduce artifacts and improve the quality of compressed video. The deblocking filter in VVC still adopts the method based on empirical threshold when making the filtering decision, but the actual video scene changes variously, and it is obviously impossible to obtain the optimal filtering effect with a fixed threshold. The decision of the filtering mode is essentially a classification problem. Existing research shows that the convolutional neural network (CNN) has a strong ability in classification tasks, and its feature extraction and nonlinear fitting ability can greatly improve the learning ability of the target, so as to obtain better classification accuracy. Therefore, we improves the deblocking filter in VVC based on CNN. Experimental results show that, compared with the original filtering method of VVC, our method can better improve the quality of compressed video.