Leveraging Video Coding Knowledge for Deep Video Enhancement

Autor:	Bach, Thong, Canh, Thuong Nguyen, Nguyen, Van-Quang
Rok vydání:	2023
Předmět:	Electrical Engineering and Systems Science - Image and Video Processing Computer Science - Computer Vision and Pattern Recognition
Druh dokumentu:	Working Paper
Popis:	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. Comment: for NTIRE2 challenge in CVPR22
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2302.13594 Zobrazit plný text záznamu View this record from Arxiv