Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/32084
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dc.contributor.authorZhang, Len_US
dc.contributor.authorZhang, Len_US
dc.contributor.authorMou, Xen_US
dc.contributor.authorZhang, Den_US
dc.date.accessioned2014-12-31T08:01:16Z-
dc.date.available2014-12-31T08:01:16Z-
dc.date.issued2011-
dc.identifier.citationIEEE transactions on image processing, 2011, v. 20, no. 8, 5705575, p. 2378-2386en_US
dc.identifier.issn1057-7149-
dc.identifier.urihttp://hdl.handle.net/10397/32084-
dc.description.abstractImage quality assessment (IQA) aims to use computational models to measure the image quality consistently with subjective evaluations. The well-known structural similarity index brings IQA from pixel- to structure-based stage. In this paper, a novel feature similarity (FSIM) index for full reference IQA is proposed based on the fact that human visual system (HVS) understands an image mainly according to its low-level features. Specifically, the phase congruency (PC), which is a dimensionless measure of the significance of a local structure, is used as the primary feature in FSIM. Considering that PC is contrast invariant while the contrast information does affect HVS' perception of image quality, the image gradient magnitude (GM) is employed as the secondary feature in FSIM. PC and GM play complementary roles in characterizing the image local quality. After obtaining the local quality map, we use PC again as a weighting function to derive a single quality score. Extensive experiments performed on six benchmark IQA databases demonstrate that FSIM can achieve much higher consistency with the subjective evaluations than state-of-the-art IQA metrics.en_US
dc.description.sponsorshipDepartment of Computingen_US
dc.language.isoenen_US
dc.publisherInstitute of Electrical and Electronics Engineersen_US
dc.relation.ispartofIEEE transactions on image processingen_US
dc.subjectGradienten_US
dc.subjectImage quality assessment (IQA)en_US
dc.subjectLow-level featureen_US
dc.subjectPhase congruency (PC)en_US
dc.titleFSIM : a feature similarity index for image quality assessmenten_US
dc.typeJournal/Magazine Articleen_US
dc.identifier.spage2378-
dc.identifier.epage2386-
dc.identifier.volume20-
dc.identifier.issue8-
dc.identifier.doi10.1109/TIP.2011.2109730-
dc.identifier.isiWOS:000293692300026-
dc.identifier.scopus2-s2.0-79960509746-
dc.identifier.pmid21292594-
dc.identifier.rosr56213-
dc.identifier.eissn1941-0042-
item.fulltextFull Text (via PolyU elinks)-
crisitem.author.deptDepartment of Computing-
crisitem.author.deptDepartment of Computing-
crisitem.author.facultyFaculty of Engineering-
crisitem.author.facultyFaculty of Engineering-
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