LIVE-FB large-scale Social Picture Quality Database and deep image quality model creation

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dc.contributor.advisorBovik, Alan C. (Alan Conrad), 1958-
dc.creatorNiu, Haoran
dc.creator.orcid0000-0003-4596-6127
dc.date.accessioned2023-01-06T22:34:00Z
dc.date.available2023-01-06T22:34:00Z
dc.date.created2021-12
dc.date.issued2022-03-04
dc.date.submittedDecember 2021
dc.date.updated2023-01-06T22:34:01Z
dc.description.abstractImage quality assessment (IQA), especially perceptual image IQA, has been researched for years. Automatic human perceptual quality is hard because when people evaluate picture quality, there are many unpredictable factors affecting their final scores. Therefore, it is necessary to build a large dataset for model training. It is costly to build such a dataset as both human annotation collection and data processing steps are tedious. In that case, there are not many datasets ever tested on large databases. In this paper, we will focus on the process of building the LIVE-FB large-scale Social Picture Quality Database, a deep IQA model evaluation, and new IQA model proposal.
dc.description.departmentElectrical and Computer Engineering
dc.format.mimetypeapplication/pdf
dc.identifier.urihttps://hdl.handle.net/2152/117141
dc.identifier.urihttp://dx.doi.org/10.26153/tsw/44035
dc.language.isoen
dc.subjectImage Quality Assessment
dc.subjectLarge-scale database
dc.subjectHuman study
dc.subjectDeep learning techniques
dc.titleLIVE-FB large-scale Social Picture Quality Database and deep image quality model creation
dc.typeThesis
dc.type.materialtext
local.embargo.lift2023-12-01
local.embargo.terms2023-12-01
thesis.degree.departmentElectrical and Computer Engineering
thesis.degree.disciplineElectrical and Computer Engineering
thesis.degree.grantorThe University of Texas at Austin
thesis.degree.levelMasters
thesis.degree.nameMaster of Science in Engineering

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