EHNQ: Subjective and objective quality evaluation of enhanced night-time images

Yang, Ying, Xiang, Tao, Guo, Shangwei, Lv, Xiao, Liu, Hantao ORCID: https://orcid.org/0000-0003-4544-3481 and Liao, Xiaofeng 2023. EHNQ: Subjective and objective quality evaluation of enhanced night-time images. IEEE Transactions on Circuits and Systems for Video Technology 33 (9) , pp. 4645-4659. 10.1109/TCSVT.2023.3245625

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Abstract

Vision-based practical applications, such as consumer photography and automated driving systems, greatly rely on enhancing the visibility of images captured in night-time environments. For this reason, various image enhancement algorithms (EHAs) have been proposed. However, little attention has been given to the quality evaluation of enhanced night-time images. In this paper, we conduct the first dedicated exploration of the subjective and objective quality evaluation of enhanced night-time images. First, we build an enhanced night-time image quality (EHNQ) database, which is the largest of its kind so far. It includes 1,500 enhanced images generated from 100 real night-time images using 15 different EHAs. Subsequently, we perform a subjective quality evaluation and obtain subjective quality scores on the EHNQ database. Thereafter, we present an objective blind quality index for enhanced night-time images (BEHN). Enhanced night-time images usually suffer from inappropriate brightness and contrast, deformed structure, and unnatural colorfulness. In BEHN, we capture perceptual features that are highly relevant to these three types of corruptions, and we design an ensemble training strategy to map the extracted features into the quality score. Finally, we conduct extensive experiments on EHNQ and EAQA databases. The experimental and analysis results validate the performance of the proposed BEHN compared with the state-of-the-art approaches. Our EHNQ database is publicly available for download at https://sites.google.com/site/xiangtaooo/.

Item Type:	Article
Date Type:	Publication
Status:	Published
Schools:	Computer Science & Informatics
Publisher:	Institute of Electrical and Electronics Engineers
ISSN:	1051-8215
Date of First Compliant Deposit:	27 February 2023
Date of Acceptance:	4 February 2023
Last Modified:	05 Oct 2023 21:28
URI:	https://orca.cardiff.ac.uk/id/eprint/157382

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