NTIRE 2021 challenge on perceptual image quality assessment

Gu, J; Cai, H Dong, C Ren, J.S Qiao, Y Gu, S Timofte, R Cheon, M Yoon, S Kang, B. K Lee, J Zhang, Q Guo, H Bin, Y Hou, Y Luo, H Guo, J Wang, Z Wang, H Yang, W Bai, Q Shi, S Xia, W Cao, M Wang, J Chen, Y Yang, Y Li, Y Zhang, T Feng, L Liao, Y Li, J Thong, W Pereira, J. C Leonardis, A McDonagh, S Xu, K Yang, L Cai, H Sun, P Ayyoubzadeh, M Royat, A Fezza, A Hammou, D Hamidouche, W Ahn, S Yoon, G Tsubota, K Akutsu, H Aizawa, K Sharif University of Technology

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NTIRE 2021 challenge on perceptual image quality assessment

Gu, J ; Sharif University of Technology | 2021

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Type of Document: Article
DOI: 10.1109/CVPRW53098.2021.00077
Publisher: IEEE Computer Society , 2021
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. © 2021 IEEE
Keywords:
Computer vision ; Image enhancement ; Image quality ; Image reconstruction ; Quality control ; Subjective testing ; Textures ; Image processing algorithm ; Image processing technology ; Image quality assessment ; Image restoration and enhancements ; Network-based ; Participating teams ; Perceptual image processing ; Perceptual image quality ; Training and testing ; Visual qualities ; Generative adversarial networks
Source: 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2021, 19 June 2021 through 25 June 2021 ; 2021 , Pages 677-690 ; 21607508 (ISSN); 9781665448994 (ISBN)
URL: https://ieeexplore.ieee.org/document/9522906

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