Details

Autor(en) / Beteiligte

• Gu, Jinjin
• Cai, Haoming
• Dong, Chao
• Ren, Jimmy S.
• Qiao, Yu
• Gu, Shuhang
• Timofte, Radu
• Cheon, Manri
• Yoon, Sungjun
• Kang, Byungyeon Kangg
• Lee, Junwoo
• Zhang, Qing
• Guo, Haiyang
• Bin, Yi
• Hou, Yuqing
• Luo, Hengliang
• Guo, Jingyu
• Wang, Zirui
• Wang, Hai
• Yang, Wenming
• Bai, Qingyan
• Shi, Shuwei
• Xia, Weihao
• Cao, Mingdeng
• Wang, Jiahao
• Chen, Yifan
• Yang, Yujiu
• Li, Yang
• Zhang, Tao
• Feng, Longtao
• Liao, Yiting
• Li, Junlin
• Thong, William
• Pereira, Jose Costa
• Leonardis, Ales
• McDonagh, Steven
• Xu, Kele
• Yang, Lehan
• Cai, Hengxing
• Sun, Pengfei
• Ayyoubzadeh, Seyed Mehdi
• Royat, Ali
• Fezza, Sid Ahmed
• Hammou, Dounia
• Hamidouche, Wassim
• Ahn, Sewoong
• Yoon, Gwangjin
• Tsubota, Koki
• Akutsu, Hiroaki
• Aizawa, Kiyoharu

Titel

NTIRE 2021 Challenge on Perceptual Image Quality Assessment

Ist Teil von

• 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 2021, p.677-690

Ort / Verlag

IEEE

Erscheinungsjahr

2021

Quelle

IEEE Electronic Library Online

Beschreibungen/Notizen

• This paper reports on the NTIRE 2021 challenge on perceptual image quality assessment (IQA), held in conjunction with the New Trends in Image Restoration and Enhancement workshop (NTIRE) workshop at CVPR 2021. As a new type of image processing technology, perceptual image processing algorithms based on Generative Adversarial Networks (GAN) have produced images with more realistic textures. These output images have completely different characteristics from traditional distortions, thus pose a new challenge for IQA methods to evaluate their visual quality. In comparison with previous IQA challenges, the training and testing datasets in this challenge include the outputs of perceptual image processing algorithms and the corresponding subjective scores. Thus they can be used to develop and evaluate IQA methods on GAN-based distortions. The challenge has 270 registered participants in total. In the final testing stage, 13 participating teams submitted their models and fact sheets. Almost all of them have achieved much better results than existing IQA methods, while the winning method can demonstrate state-of-the-art performance.