Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/99259
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dc.contributor.authorLiu, Anminen
dc.contributor.authorLin, Weisien
dc.contributor.authorNarwaria, Manishen
dc.date.accessioned2013-09-19T01:35:41Zen
dc.date.accessioned2019-12-06T20:05:09Z-
dc.date.available2013-09-19T01:35:41Zen
dc.date.available2019-12-06T20:05:09Z-
dc.date.copyright2011en
dc.date.issued2011en
dc.identifier.citationLiu, A., Lin, W., & Narwaria, M. (2011). Image quality assessment based on gradient similarity. IEEE transactions on image processing, 21(4), 1500-1512.en
dc.identifier.issn1057-7149en
dc.identifier.urihttps://hdl.handle.net/10356/99259-
dc.description.abstractIn this paper, we propose a new image quality assessment (IQA) scheme, with emphasis on gradient similarity. Gradients convey important visual information and are crucial to scene understanding. Using such information, structural and contrast changes can be effectively captured. Therefore, we use the gradient similarity to measure the change in contrast and structure in images. Apart from the structural/contrast changes, image quality is also affected by luminance changes, which must be also accounted for complete and more robust IQA. Hence, the proposed scheme considers both luminance and contrast-structural changes to effectively assess image quality. Furthermore, the proposed scheme is designed to follow the masking effect and visibility threshold more closely, i.e., the case when both masked and masking signals are small is more effectively tackled by the proposed scheme. Finally, the effects of the changes in luminance and contrast-structure are integrated via an adaptive method to obtain the overall image quality score. Extensive experiments conducted with six publicly available subject-rated databases (comprising of diverse images and distortion types) have confirmed the effectiveness, robustness, and efficiency of the proposed scheme in comparison with the relevant state-of-the-art schemes.en
dc.language.isoenen
dc.relation.ispartofseriesIEEE transactions on image processingen
dc.rights© 2011 IEEEen
dc.subjectDRNTU::Engineering::Computer science and engineering::Computing methodologies::Image processing and computer visionen
dc.titleImage quality assessment based on gradient similarityen
dc.typeJournal Articleen
dc.contributor.schoolSchool of Computer Engineeringen
dc.identifier.doi10.1109/TIP.2011.2175935en
item.fulltextNo Fulltext-
item.grantfulltextnone-
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