NTIRE 2023 Quality Assessment of Video Enhancement Challenge
Xiaohong Liu Xiongkuo Min Wei Sun Yulun Zhang Kai Zhang Radu Timofte Guangtao Zhai Yixuan Gao Yuqin Cao Tengchuan Kou Yunlong Dong Ziheng Jia Yilin Li Wei Wu Shuming Hu Sibin Deng Pengxiang Xiao Ying Chen Kai Li Kai Zhao Kun Yuan Ming Sun Heng Cong Hao Wang Lingzhi Fu Yusheng Zhang Rongyu Zhang Hang Shi Qihang Xu Longan Xiao Zhiliang Ma Mirko Agarla Luigi Celona Claudio Rota Raimondo Schettini Zhiwei Huang Yanan Li Xiaotao Wang Lei Lei Hongye Liu Wei Hong Ironhead Chuang Allen Lin Drake Guan Iris Chen Kae Lou Willy Huang Yachun Tasi Yvonne Kao Haotian Fan Fangyuan Kong Shiqi Zhou Hao Liu Yu Lai Shanshan Chen Wenqi Wang Haoning Wu Chaofeng Chen Chunzheng Zhu Zekun Guo Shiling Zhao Haibing Yin Hongkui Wang Hanene Brachemi Meftah Sid Ahmed Fezza Wassim Hamidouche Olivier Déforges Tengfei Shi Azadeh Mansouri Hossein Motamednia Amir Hossein Bakhtiari Ahmad Mahmoudi Aznaveh
This paper reports on the NTIRE 2023 Quality Assessment of Video Enhancement
Challenge, which will be held in conjunction with the New Trends in Image
Restoration and Enhancement Workshop (NTIRE) at CVPR 2023. This challenge is to
address a major challenge in the field of video processing, namely, video
quality assessment (VQA) for enhanced videos. The challenge uses the VQA
Dataset for Perceptual Video Enhancement (VDPVE), which has a total of 1211
enhanced videos, including 600 videos with color, brightness, and contrast
enhancements, 310 videos with deblurring, and 301 deshaked videos. The
challenge has a total of 167 registered participants. 61 participating teams
submitted their prediction results during the development phase, with a total
of 3168 submissions. A total of 176 submissions were submitted by 37
participating teams during the final testing phase. Finally, 19 participating
teams submitted their models and fact sheets, and detailed the methods they
used. Some methods have achieved better results than baseline methods, and the
winning methods have demonstrated superior prediction performance.