Super-Resolving Compressed Video in Coding Chain
This work addresses video quality enhancement for transmission and storage applications, representing an incremental improvement over existing methods.
The paper tackles the problem of enhancing resolution in compressed videos by addressing interference between resolution loss and compression artifacts, resulting in improved perceptual quality through a mixed-resolution coding framework with a reference-based DCNN.
Scaling and lossy coding are widely used in video transmission and storage. Previous methods for enhancing the resolution of such videos often ignore the inherent interference between resolution loss and compression artifacts, which compromises perceptual video quality. To address this problem, we present a mixed-resolution coding framework, which cooperates with a reference-based DCNN. In this novel coding chain, the reference-based DCNN learns the direct mapping from low-resolution (LR) compressed video to their high-resolution (HR) clean version at the decoder side. We further improve reconstruction quality by devising an efficient deformable alignment module with receptive field block to handle various motion distances and introducing a disentangled loss that helps networks distinguish the artifact patterns from texture. Extensive experiments demonstrate the effectiveness of proposed innovations by comparing with state-of-the-art single image, video and reference-based restoration methods.