Leveraging Video Coding Knowledge for Deep Video Enhancement
- URL: http://arxiv.org/abs/2302.13594v1
- Date: Mon, 27 Feb 2023 09:00:29 GMT
- Title: Leveraging Video Coding Knowledge for Deep Video Enhancement
- Authors: Thong Bach, Thuong Nguyen Canh, Van-Quang Nguyen
- Abstract summary: This study proposes a novel framework that leverages the low-delay configuration of video compression to enhance the existing state-of-the-art method, BasicVSR++.
We incorporate a context-adaptive video fusion method to enhance the final quality of compressed videos.
The proposed approach has been evaluated in the NTIRE22 challenge, a benchmark for video restoration and enhancement, and achieved improvements in both quantitative metrics and visual quality compared to the previous method.
- Score: 6.746400031322727
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Recent advancements in deep learning techniques have significantly improved
the quality of compressed videos. However, previous approaches have not fully
exploited the motion characteristics of compressed videos, such as the drastic
change in motion between video contents and the hierarchical coding structure
of the compressed video. This study proposes a novel framework that leverages
the low-delay configuration of video compression to enhance the existing
state-of-the-art method, BasicVSR++. We incorporate a context-adaptive video
fusion method to enhance the final quality of compressed videos. The proposed
approach has been evaluated in the NTIRE22 challenge, a benchmark for video
restoration and enhancement, and achieved improvements in both quantitative
metrics and visual quality compared to the previous method.
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