Perceptual Quality Assessment of Pan-sharpened Images

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dc.contributor.advisorBenítez Restrepo, Hernán Daríospa
dc.contributor.authorAgudelo Medina, Oscar Andrésspa
dc.date.accessioned2023-03-02T01:34:32Z
dc.date.accessioned2023-02-02spa
dc.date.accessioned2023-11-24T07:45:33Z
dc.date.available2023-03-02T01:34:32Z
dc.date.available2023-11-24T07:45:33Z
dc.date.issued2018spa
dc.description.abstractPan-sharpening (PS) is an approach to fuse the spatial details of a high-resolution panchromatic (PAN) image and the spectral information of a low- resolution multispectral (MS) image. PS is a preliminary step for enhancing images for remote sensing tasks, such as change detection, object recognition, visual image analysis, and scene interpretation. Given the need for selecting pan-sharpening techniques that provide better spatial and spectral quality of pan-sharpened images, it is highly desirable to be able to automatically and accurately predict pan-sharpened image quality, as would be perceived and reported by human beings and evaluating at the same time spectral distortions as color changes in the PS image. In this research we propose a new image quality assessment (IQA) measure that uses the statistics of natural images, commonly referred to as natural scene statistics (NSS) to extract statistical regularities from PS images. NSS are measurably modified by the presence of distortions, we take advantage of this behavior to characterize some relevant distortions presented in PS images. We analyze six PS methods in the presence of two common distortions, blur, and white noise, on PAN images. Furthermore, we conducted a human study on the subjective quality of pristine and degraded PS images and created a completely blind fused image quality analyzer. In this test, 33 subjects evaluated 420 images in five sessions. In addition, we propose an opinion aware fused image quality analyzer, whose relative predictions with respect to other models match better to human perceptual evaluations than state-of-the-art reduced and full resolution quality metrics. An implementation of the results of subjective study and the proposed fused image quality measures can be found at https://github.com/oscaragudelom/Pansharpening-IQA.spa
dc.formatapplication/pdfspa
dc.format.mimetypeapplication/pdfspa
dc.identifier.urihttps://vitela.javerianacali.edu.co/handle/11522/740
dc.language.isospaspa
dc.publisherPontificia Universidad Javerianaspa
dc.publisher.placeCalispa
dc.rightsinfo:eu-repo/semantics/openAccessspa
dc.rights.accessRightshttp://purl.org/coar/access_right/c_abf2spa
dc.rights.creativecommonshttps://creativecommons.org/licenses/by-nc/4.0/spa
dc.rights.urihttps://creativecommons.org/licenses/by-nc/4.0/spa
dc.sourcePontificia Universidad Javerianaspa
dc.sourceVitelaspa
dc.subject.proposalFacultad de Ingenieríaspa
dc.subject.proposalMaestría en ingeniería con Énfasis en Ingeniería Electrónicaspa
dc.subject.proposalTwitterspa
dc.subject.proposalSocial networkspa
dc.subject.proposalInformation spreadingspa
dc.subject.proposalCoveragespa
dc.subject.proposalVoronoispa
dc.subject.proposalGraph theoryspa
dc.titlePerceptual Quality Assessment of Pan-sharpened Imagesspa
dc.typeMaestríaspa
dc.typeMaestría en ingeniería Electronicaspa
dc.type.coarhttp://purl.org/coar/resource_type/c_bdccspa
dc.type.driverinfo:eu-repo/semantics/masterThesisspa
dc.type.hasversioninfo:eu-repo/semantics/acceptedVersionspa
dc.type.localTesis/Trabajo de grado - Monografía - Maestríaspa
dc.type.redcolhttps://purl.org/redcol/resource_type/TMspa
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