  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.
