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Study of natural scene categories in measurement of perceived image quality

Yang, Xiaohan, Li, Fan, Li, Leida, Gu, Ke and Liu, Hantao ORCID: 2022. Study of natural scene categories in measurement of perceived image quality. IEEE Transactions on Instrumentation and Measurement 71 10.1109/TIM.2022.3154808

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One challenge facing image quality assessment (IQA) is that current models designed or trained on the basis of exiting databases are intrinsically suboptimal and cannot deal with the real-world complexity and diversity of natural scenes. IQA models and databases are heavily skewed toward the visibility of distortions. It is critical to understand the wider determinants of perceived quality and use the new understanding to improve the predictive power of IQA models. Human behavioral categorization performance is powerful and essential for visual tasks. However, little is known about the impact of natural scene categories (SCs) on perceived image quality. We hypothesize that different classes of natural scenes influence image quality perception—how image quality is perceived is not only affected by the lower level image statistics and image structures shared between different categories but also by the semantic distinctions between these categories. In this article, we first design and conduct a fully controlled psychovisual experiment to verify our hypothesis. Then, we propose a computational framework that integrates the natural SC-specific component into image quality prediction. Research demonstrates the importance and plausibility of considering natural SCs in future IQA databases and models.

Item Type: Article
Date Type: Publication
Status: Published
Schools: Computer Science & Informatics
Publisher: Institute of Electrical and Electronics Engineers
ISSN: 0018-9456
Date of First Compliant Deposit: 15 February 2022
Date of Acceptance: 13 February 2022
Last Modified: 06 Nov 2023 13:54

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