Perceptual representations of structural information in images: application to quality assessment of synthesized view in FTV scenario

التفاصيل البيبلوغرافية
العنوان: Perceptual representations of structural information in images: application to quality assessment of synthesized view in FTV scenario
المؤلفون: suiyi, Ling, Jing, Li, Patrick, Le Callet, Junle, Wang
سنة النشر: 2019
المجموعة: Computer Science
مصطلحات موضوعية: Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition
الوصف: As the immersive multimedia techniques like Free-viewpoint TV (FTV) develop at an astonishing rate, user's demand for high-quality immersive contents increases dramatically. Unlike traditional uniform artifacts, the distortions within immersive contents could be non-uniform structure-related and thus are challenging for commonly used quality metrics. Recent studies have demonstrated that the representation of visual features can be extracted from multiple levels of the hierarchy. Inspired by the hierarchical representation mechanism in the human visual system (HVS), in this paper, we explore to adopt structural representations to quantitatively measure the impact of such structure-related distortion on perceived quality in FTV scenario. More specifically, a bio-inspired full reference image quality metric is proposed based on 1) low-level contour descriptor; 2) mid-level contour category descriptor; and 3) task-oriented non-natural structure descriptor. The experimental results show that the proposed model outperforms significantly the state-of-the-art metrics.
نوع الوثيقة: Working Paper
URL الوصول: http://arxiv.org/abs/1907.03448
رقم الانضمام: edsarx.1907.03448
قاعدة البيانات: arXiv
ResultId 1
Header edsarx
arXiv
edsarx.1907.03448
994
3
Report
report
993.768676757813
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&scope=site&db=edsarx&AN=edsarx.1907.03448&custid=s6537998&authtype=sso
FullText Array ( [Availability] => 0 )
Array ( [0] => Array ( [Url] => http://arxiv.org/abs/1907.03448 [Name] => EDS - Arxiv [Category] => fullText [Text] => View record in Arxiv [MouseOverText] => View record in Arxiv ) )
Items Array ( [Name] => Title [Label] => Title [Group] => Ti [Data] => Perceptual representations of structural information in images: application to quality assessment of synthesized view in FTV scenario )
Array ( [Name] => Author [Label] => Authors [Group] => Au [Data] => <searchLink fieldCode="AR" term="%22suiyi%2C+Ling%22">suiyi, Ling</searchLink><br /><searchLink fieldCode="AR" term="%22Jing%2C+Li%22">Jing, Li</searchLink><br /><searchLink fieldCode="AR" term="%22Patrick%2C+Le+Callet%22">Patrick, Le Callet</searchLink><br /><searchLink fieldCode="AR" term="%22Junle%2C+Wang%22">Junle, Wang</searchLink> )
Array ( [Name] => DatePubCY [Label] => Publication Year [Group] => Date [Data] => 2019 )
Array ( [Name] => Subset [Label] => Collection [Group] => HoldingsInfo [Data] => Computer Science )
Array ( [Name] => Subject [Label] => Subject Terms [Group] => Su [Data] => <searchLink fieldCode="DE" term="%22Electrical+Engineering+and+Systems+Science+-+Image+and+Video+Processing%22">Electrical Engineering and Systems Science - Image and Video Processing</searchLink><br /><searchLink fieldCode="DE" term="%22Computer+Science+-+Computer+Vision+and+Pattern+Recognition%22">Computer Science - Computer Vision and Pattern Recognition</searchLink> )
Array ( [Name] => Abstract [Label] => Description [Group] => Ab [Data] => As the immersive multimedia techniques like Free-viewpoint TV (FTV) develop at an astonishing rate, user's demand for high-quality immersive contents increases dramatically. Unlike traditional uniform artifacts, the distortions within immersive contents could be non-uniform structure-related and thus are challenging for commonly used quality metrics. Recent studies have demonstrated that the representation of visual features can be extracted from multiple levels of the hierarchy. Inspired by the hierarchical representation mechanism in the human visual system (HVS), in this paper, we explore to adopt structural representations to quantitatively measure the impact of such structure-related distortion on perceived quality in FTV scenario. More specifically, a bio-inspired full reference image quality metric is proposed based on 1) low-level contour descriptor; 2) mid-level contour category descriptor; and 3) task-oriented non-natural structure descriptor. The experimental results show that the proposed model outperforms significantly the state-of-the-art metrics. )
Array ( [Name] => TypeDocument [Label] => Document Type [Group] => TypDoc [Data] => Working Paper )
Array ( [Name] => URL [Label] => Access URL [Group] => URL [Data] => <link linkTarget="URL" linkTerm="http://arxiv.org/abs/1907.03448" linkWindow="_blank">http://arxiv.org/abs/1907.03448</link> )
Array ( [Name] => AN [Label] => Accession Number [Group] => ID [Data] => edsarx.1907.03448 )
RecordInfo Array ( [BibEntity] => Array ( [Subjects] => Array ( [0] => Array ( [SubjectFull] => Electrical Engineering and Systems Science - Image and Video Processing [Type] => general ) [1] => Array ( [SubjectFull] => Computer Science - Computer Vision and Pattern Recognition [Type] => general ) ) [Titles] => Array ( [0] => Array ( [TitleFull] => Perceptual representations of structural information in images: application to quality assessment of synthesized view in FTV scenario [Type] => main ) ) ) [BibRelationships] => Array ( [HasContributorRelationships] => Array ( [0] => Array ( [PersonEntity] => Array ( [Name] => Array ( [NameFull] => suiyi, Ling ) ) ) [1] => Array ( [PersonEntity] => Array ( [Name] => Array ( [NameFull] => Jing, Li ) ) ) [2] => Array ( [PersonEntity] => Array ( [Name] => Array ( [NameFull] => Patrick, Le Callet ) ) ) [3] => Array ( [PersonEntity] => Array ( [Name] => Array ( [NameFull] => Junle, Wang ) ) ) ) [IsPartOfRelationships] => Array ( [0] => Array ( [BibEntity] => Array ( [Dates] => Array ( [0] => Array ( [D] => 08 [M] => 07 [Type] => published [Y] => 2019 ) ) ) ) ) ) )
IllustrationInfo