Super-Resolving Compressed Video in Coding Chain
Dewang Hou, Yang Zhao, Yuyao Ye, Jiayu Yang, Jian Zhang, Ronggang Wang

TL;DR
This paper introduces a mixed-resolution coding framework with a reference-based deep neural network that enhances the resolution of compressed videos by effectively handling artifacts and motion, improving perceptual quality.
Contribution
The paper proposes a novel coding chain integrating a reference-based DCNN with deformable alignment and disentangled loss for superior video super-resolution in compressed videos.
Findings
Outperforms state-of-the-art methods in resolution enhancement
Effectively handles motion and artifacts in compressed videos
Improves perceptual quality of reconstructed videos
Abstract
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…
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Taxonomy
TopicsAdvanced Image Processing Techniques · Video Coding and Compression Technologies · Image and Signal Denoising Methods
MethodsDiffusion-Convolutional Neural Networks
