Mitigating Delivery Artifacts in Real-World Video Super-Resolution

Over the past few decades, Internet video streaming has seen explosive growth, pushing network resources to their limits. Video Super-Resolution (VSR) technology, which enhances video quality while reducing bandwidth usage, offers a promising solution to replace traditional video delivery frameworks. However, existing real-world VSR approaches often struggle when faced with inevitable packet loss during network transmission, especially in bandwidth-constrained and low-latency environments. This packet loss introduces amplified noise and artifacts, significantly degrading visual quality. In this work, we decouple the various degrees of degradation caused by packet loss and comprehensively analyze the impact of different types of packet loss. To address these challenges, we propose ReinVSR, an efficient countermeasure strategy that mitigates the detrimental effects of packet loss without introducing additional network overhead. ReinVSR employs a two-pronged approach: a pre-restore module to mitigate missing pixel information and a Local Hidden State Attention module to rectify semantic distortions at the feature level by replacing corrupted hidden states with more accurate representations. Specifically, we leverage neighboring frames to generate a pool of hidden features, which are then refined using a novel spatial attention mechanism to aggregate more authentic and accurate hidden states. Extensive experiments demonstrate that ReinVSR outperforms state-of-the-art methods, achieving significant improvements in visual quality. It offers a robust and effective solution for high-quality video streaming in bandwidth-limited environments.