Multi-Scale Self-Attention-Based Convolutional-Neural-Network Post-Filtering for AV1 Codec: Towards Enhanced Visual Quality and Overall Coding Performance

This paper presents MS-MTSA, a multi-scale multi-type self-attention network designed to enhance AV1-compressed video through targeted post-filtering. The objective is to address two persistent artifact issues observed in our previous MTSA model: visible seams at patch boundaries and grid-like distortions from upsampling. To this end, MS-MTSA introduces two key architectural enhancements. First, multi-scale block-wise self-attention applies sequential attention over 16 × 16 and 12 × 12 blocks to better capture local context and improve spatial continuity. Second, refined patch-wise self-attention includes a lightweight convolutional refinement layer after upsampling to suppress structured artifacts in flat regions. These targeted modifications significantly improve both perceptual and quantitative quality. The proposed network achieves BD-rate reductions of 12.44% for Y, 21.70% for Cb, and 19.90% for Cr compared to the AV1 anchor. Visual evaluations confirm improved texture fidelity and reduced seam artifacts, demonstrating the effectiveness of combining multi-scale attention and structural refinement for artifact suppression in compressed video.