Existing video compression (VC) methods primarily aim to reduce the spatial and temporal redundancies between consecutive frames in a video while preserving its quality. In this regard, previous works have achieved remarkable results on videos acquired under specific settings such as instant (known) exposure time and shutter speed which often result in sharp videos. However, when these methods are evaluated on videos captured under different temporal priors, which lead to degradations like motion blur and low frame rate, they fail to maintain the quality of the contents. In this work, we tackle the VC problem in a general scenario where a given video can be blurry due to predefined camera settings or dynamics in the scene. By exploiting the natural trade-off between visual enhancement and data compression, we formulate VC as a min-max optimization problem and propose an effective framework and training strategy to tackle the problem. Extensive experimental results on several benchmark datasets confirm the effectiveness of our method compared to several state-of-the-art VC approaches.

翻译：现有的视频压缩方法主要旨在降低连续帧间的空间和时间冗余，同时保持视频质量。在这方面，先前的工作在特定设置（如即时（已知）曝光时间和快门速度）下捕获的视频上取得了显著成果，这些设置通常能生成清晰视频。然而，当这些方法应用于不同时间先验条件下捕获的视频时（这些条件会导致运动模糊和低帧率等退化），它们无法维持内容质量。在本工作中，我们针对一般场景下的视频压缩问题，其中给定视频可能因预定义的相机设置或场景动态而模糊。通过利用视觉增强与数据压缩之间的自然权衡，我们将视频压缩表述为一个最小-最大优化问题，并提出一个有效框架和训练策略来解决该问题。在多个基准数据集上的广泛实验结果表明，与几种最先进的视频压缩方法相比，我们的方法具有有效性。