Degradation-Aware Hierarchical Termination for Blind Quality Enhancement of Compressed Video
Li Yu, Yingbo Zhao, Shiyu Wu, Siyue Yu, Moncef Gabbouj, and Qingshan Liu

TL;DR
This paper introduces a degradation-aware hierarchical termination method for blind compressed video quality enhancement, utilizing multiscale degradation representations and dynamic stage adjustment to improve performance and efficiency.
Contribution
It proposes a pretrained degradation representation learning module and a hierarchical termination mechanism, addressing limitations of existing blind QECV methods and adapting to different compression levels.
Findings
Achieved 110% PSNR improvement over state-of-the-art blind methods at QP=22.
Reduced inference time by 50% at QP=22 compared to QP=42.
Effectively captures multiscale degradation information for artifact removal.
Abstract
Existing studies on Quality Enhancement for Compressed Video (QECV) predominantly rely on known Quantization Parameters (QPs), employing distinct enhancement models per QP setting, termed non-blind methods. However, in real-world scenarios involving transcoding or transmission, QPs may be partially or entirely unknown, limiting the applicability of such approaches and motivating the development of blind QECV techniques. Current blind methods generate degradation vectors via classification models with cross-entropy loss, using them as channel attention to guide artifact removal. However, these vectors capture only global degradation information and lack spatial details, hindering adaptation to varying artifact patterns at different spatial positions. To address these limitations, we propose a pretrained Degradation Representation Learning (DRL) module that decouples and extracts…
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Taxonomy
TopicsImage and Video Quality Assessment · Video Coding and Compression Technologies · Advanced Image Processing Techniques
