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A saliency dispersion measure for improving saliency-based image quality metrics

Zhang, Wei, Martin, Ralph R. and Liu, Hantao ORCID: 2018. A saliency dispersion measure for improving saliency-based image quality metrics. IEEE Transactions on Circuits and Systems for Video Technology 28 (6) , pp. 1462-1466. 10.1109/TCSVT.2017.2650910

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Objective image quality metrics (IQMs) potentially benefit from the addition of visual saliency. However, challenges to optimising the performance of saliency-based IQMs remain. A previous eye-tracking study has shown that gaze is concentrated in fewer places in images with highly salient features than in images lacking salient features. From this, it can be inferred that the former are more likely to benefit from adding a saliency term to an IQM. To understand whether these ideas still hold when using computational saliency instead of eyetracking data, we first conducted a statistical evaluation using 15 state of the art saliency models and 10 well-known IQMs. We then used the results to devise an algorithm which adaptively incorporates saliency in IQMs for natural scenes, based on saliency dispersion. Experimental results demonstrate this can give significant improvements

Item Type: Article
Date Type: Publication
Status: Published
Schools: Computer Science & Informatics
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Additional Information: This work is licensed under a Creative Commons Attribution 3.0 License. For more information, see
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
ISSN: 1051-8215
Date of First Compliant Deposit: 5 April 2017
Date of Acceptance: 27 December 2016
Last Modified: 05 May 2023 10:43

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