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NTIRE 2021 challenge on perceptual image quality assessment

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

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

47 Citations (Scopus)

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.

Original languageEnglish
Title of host publicationProceedings - 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2021
Pages677-690
Number of pages14
ISBN (Electronic)9781665448994
DOIs
Publication statusPublished - Jun 2021
Externally publishedYes
Event2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2021 - Virtual, Online
Duration: 19 Jun 202125 Jun 2021

Publication series

NameIEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops
ISSN (Print)2160-7508
ISSN (Electronic)2160-7516

Conference

Conference2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2021
CityVirtual, Online
Period19/06/2125/06/21

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