Benchmarking state-of-the-art visual saliency models for image quality assessment

Zhang, Wei, Tian, Yi, Zha, Xiaojie and Liu, Hantao ORCID: https://orcid.org/0000-0003-4544-3481 2016. Benchmarking state-of-the-art visual saliency models for image quality assessment. Presented at: 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Shanghai, China, 20-25 March 2016. 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, pp. 1090-1094. 10.1109/ICASSP.2016.7471844

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Abstract

A significant current research trend in image quality assessment is to investigate the added value of visual attention aspects. Previous approaches mainly focused on adopting a specific saliency model to improve a specific image quality metric (IQM). It is still not known yet which of the existing saliency models is generally applicable in IQMs; which of the IQMs can profit most/least from the addition of saliency; and how this improvement depends on the saliency model used and the IQM targeted. In this paper, a large-scale benchmark study is conducted to assess the capabilities and limitations of the state-of-the-art saliency models in the context of IQMs. The study provides guidance for the application of saliency models in IQMs, in terms of the effect of saliency model dependency, IQM dependency, and image distortion dependency.

Item Type:	Conference or Workshop Item (Paper)
Date Type:	Publication
Status:	Published
Schools:	Computer Science & Informatics
Publisher:	IEEE
ISBN:	978-1-4799-9988-0
ISSN:	2379-190X
Last Modified:	25 Oct 2022 13:07
URI:	https://orca.cardiff.ac.uk/id/eprint/118912

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