  This paper focuses on the task of quality enhancement for compressed videos.
Although deep network-based video restorers achieve impressive progress, most
of the existing methods lack a structured design to optimally leverage the
priors within compression codecs. Since the quality degradation of the video is
primarily induced by the compression algorithm, a new paradigm is urgently
needed for a more ``conscious'' process of quality enhancement. As a result, we
propose the Compression-Realized Deep Structural Network (CRDS), introducing
three inductive biases aligned with the three primary processes in the classic
compression codec, merging the strengths of classical encoder architecture with
deep network capabilities. Inspired by the residual extraction and domain
transformation process in the codec, a pre-trained Latent Degradation Residual
Auto-Encoder is proposed to transform video frames into a latent feature space,
and the mutual neighborhood attention mechanism is integrated for precise
motion estimation and residual extraction. Furthermore, drawing inspiration
from the quantization noise distribution of the codec, CRDS proposes a novel
Progressive Denoising framework with intermediate supervision that decomposes
the quality enhancement into a series of simpler denoising sub-tasks.
Experimental results on datasets like LDV 2.0 and MFQE 2.0 indicate our
approach surpasses state-of-the-art models.
