NTIRE 2021 Challenge on Perceptual Image Quality Assessment
- URL: http://arxiv.org/abs/2105.03072v1
- Date: Fri, 7 May 2021 05:36:54 GMT
- Title: NTIRE 2021 Challenge on Perceptual Image Quality Assessment
- Authors: Jinjin Gu and Haoming Cai and Chao Dong and Jimmy S. Ren and Yu Qiao
and Shuhang Gu and Radu Timofte and Manri Cheon and Sungjun Yoon and
Byungyeon Kangg Kang and Junwoo Lee and Qing Zhang and Haiyang Guo and Yi Bin
and Yuqing Hou and Hengliang Luo and Jingyu Guo and Zirui Wang and Hai Wang
and Wenming Yang and Qingyan Bai and Shuwei Shi and Weihao Xia and Mingdeng
Cao and Jiahao Wang and Yifan Chen and Yujiu Yang and Yang Li and Tao Zhang
and Longtao Feng and Yiting Liao and Junlin Li and William Thong and Jose
Costa Pereira and Ales Leonardis and Steven McDonagh and Kele Xu and Lehan
Yang and Hengxing Cai and Pengfei Sun and Seyed Mehdi Ayyoubzadeh and Ali
Royat and Sid Ahmed Fezza and Dounia Hammou and Wassim Hamidouche and Sewoong
Ahn and Gwangjin Yoon and Koki Tsubota and Hiroaki Akutsu and Kiyoharu Aizawa
- Abstract summary: This paper reports on the NTIRE 2021 challenge on perceptual image quality assessment (IQA)
It was held in conjunction with the New Trends in Image Restoration and Enhancement workshop (NTIRE) 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.
- Score: 128.83256694901726
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: 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.
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