The Application Of Visual Saliency Models In Objective Image Quality Assessment: A Statistical Evaluation

Keywords

Image quality assessment; quality metric; saliency model; statistical analysis; Visual attention

Abstract

Advances in image quality assessment have shown the potential added value of including visual attention aspects in its objective assessment. Numerous models of visual saliency are implemented and integrated in different image quality metrics (IQMs), but the gain in reliability of the resulting IQMs varies to a large extent. The causes and the trends of this variation would be highly beneficial for further improvement of IQMs, but are not fully understood. In this paper, an exhaustive statistical evaluation is conducted to justify the added value of computational saliency in objective image quality assessment, using 20 state-of-the-art saliency models and 12 best-known IQMs. Quantitative results show that the difference in predicting human fixations between saliency models is sufficient to yield a significant difference in performance gain when adding these saliency models to IQMs. However, surprisingly, the extent to which an IQM can profit from adding a saliency model does not appear to have direct relevance to how well this saliency model can predict human fixations. Our statistical analysis provides useful guidance for applying saliency models in IQMs, in terms of the effect of saliency model dependence, IQM dependence, and image distortion dependence. The testbed and software are made publicly available to the research community.

Publication Date

6-1-2016

Publication Title

IEEE Transactions on Neural Networks and Learning Systems

Volume

27

Issue

6

Number of Pages

1266-1278

Document Type

Article

Personal Identifier

scopus

DOI Link

https://doi.org/10.1109/TNNLS.2015.2461603

Socpus ID

84939181488 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/84939181488

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