Generalized Compressed Video Restoration by Multi-Scale Temporal Fusion and Hierarchical Quality Score Estimation

Learning-based methods have achieved excellent performance for compressed video restoration (CVR) in recent years. However, existing networks aggregate multi-frame information inefficiently and are usually developed for specific quantization parameters (QPs), which are not convenient for practical usage. Moreover, current works only consider compressed video restoration in Constant QP (CQP) setting, but do not discuss the performance of the model in more realistic scenarios, e.g., Constant Rate Factor (CRF) and Constant Bitrate (CBR). In this paper, we propose a generalized quality-aware compressed video restoration network, namely QCRN. Specifically, to achieve multi-frame aggregation efficiently, we propose a multi-scale deformable temporal fusion. Meanwhile, QCRN decouples the global quality and local quality representations from input via the hierarchical quality score estimator, and then employs them to adjust the feature enhancement. Extensive experiments on compressed videos in various settings demonstrate that our proposed QCRN achieves favorable performance against state-of-the-art methods in terms of both quantitative metrics and visual quality.