Preselection based subjective preference evaluation for the quality of underwater images

Yang, Miao, Du, Yixiang, Huang, Yue, Liu, Hantao ORCID: https://orcid.org/0000-0003-4544-3481, Wei, Zhiqiang, Hu, Jintong, Hu, Ke and Sheng, Zhibin 2019. Preselection based subjective preference evaluation for the quality of underwater images. Presented at: IEEE Conference on Computer Vision and Pattern Recognition, Long Beach, CA, 16 - 20 June 2019. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops. IEEE, pp. 34-43.

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Abstract

Underwater images contain an interactive mixture of distortions due to the physicochemical property of water and the instability of imaging systems, which differ from those in natural images. We cannot obtain the pristine underwater image as the reference applied in the traditional benchmark databases, and the groups of gradual distortions either. In this paper, a novel preselection based preference label evaluation method is proposed to construct a combined subjective test procedure for an extended preference judgment dataset of underwater images. To the best of our knowledge, this is the first subjective evaluation procedure for underwater images, and also a solution for an expanding visual preference benchmark database. We demonstrate the excellent correlation of the proposed subjective evaluation with the traditional image quality assessment. It is also proven that the proposed subjective evaluation procedure could reflect the slight change of image quality and the authentic quality of a picture more accurately better than the traditional methods. Keywords: Image quality evaluation, MOS, underwater image, subjective image quality database

Item Type:	Conference or Workshop Item (Paper)
Status:	Published
Schools:	Computer Science & Informatics
Publisher:	IEEE
Last Modified:	12 Sep 2024 09:15
URI:	https://orca.cardiff.ac.uk/id/eprint/123837

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