Reference-based In-loop Filter with Robust Neural Feature Transfer for Video Coding

In this article, we propose an efficient reference-based deep in-loop filtering method for video coding. Existing reference-based in-loop filters often face challenges in improving coding efficiency due to the difficulty in capturing relevant textures from the reference frames. Our method accurately predicts the texture of a reference block and uses this information to restore the current block. To achieve this, we develop a reference-to-current feature estimation module that conveys high-quality information from previously coded frames in the feature domain, thereby preventing loss of detail due to inaccurate prediction. Although a neural network is trained to restore a coded video frame to be similar to the current frame, their performance can significantly degrade when operating with various quantization parameters (QPs) and managing different levels of distortion. This problem becomes further severe in the reference-to-current feature estimation, in which QP values are applied differently to video frames. We address this problem by developing a QP-aware convolution layer with a small number of learnable parameters to generate reliable features and adapt to fine-grained adaptive QPs among consecutive frames. The proposed method is implemented into the versatile video coding (VVC) reference software, VTM version 10.0. Experimental results demonstrate that the proposed method improves coding performance significantly in VVC.