论文标题
利用Bitstream元数据以快速，准确，全面的压缩视频质量增强
Leveraging Bitstream Metadata for Fast, Accurate, Generalized Compressed Video Quality Enhancement
论文作者
论文摘要
视频压缩是从社交媒体到视频会议的现代互联网动力技术的主要功能。尽管视频压缩继续成熟，但对于许多压缩设置，质量损失仍然很明显。然而，这些设置仍然具有重要的应用程序，可以在限制或以其他方式连接的带宽上有效地传输视频。在这项工作中，我们开发了一个深度学习体系结构，能够恢复细节以压缩视频，该视频利用了视频bitstream中嵌入的基础结构和运动信息。我们表明，与先前的压缩校正方法相比，这提高了恢复精度，并且与最近基于深度学习的视频压缩方法相比，竞争性的恢复准确性，同时实现了较高的吞吐量。此外，我们将模型定为量化数据，该数据在Bitstream中很容易获得。这使我们的单个模型可以处理各种不同的压缩质量设置，这些设置需要在先前的工作中进行组合。
Video compression is a central feature of the modern internet powering technologies from social media to video conferencing. While video compression continues to mature, for many compression settings, quality loss is still noticeable. These settings nevertheless have important applications to the efficient transmission of videos over bandwidth constrained or otherwise unstable connections. In this work, we develop a deep learning architecture capable of restoring detail to compressed videos which leverages the underlying structure and motion information embedded in the video bitstream. We show that this improves restoration accuracy compared to prior compression correction methods and is competitive when compared with recent deep-learning-based video compression methods on rate-distortion while achieving higher throughput. Furthermore, we condition our model on quantization data which is readily available in the bitstream. This allows our single model to handle a variety of different compression quality settings which required an ensemble of models in prior work.
