A Deep Learning Approach to No-Reference Image Quality Assessment For Monte Carlo Rendered Images

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dc.contributor.authorWhittle, Jossen_US
dc.contributor.authorJones, Mark W.en_US
dc.contributor.editor{Tam, Gary K. L. and Vidal, Francken_US
dc.date.accessioned2018-09-19T15:15:02Z
dc.date.available2018-09-19T15:15:02Z
dc.date.issued2018
dc.description.abstractIn Full-Reference Image Quality Assessment (FR-IQA) images are compared with ground truth images that are known to be of high visual quality. These metrics are utilized in order to rank algorithms under test on their image quality performance. Throughout the progress of Monte Carlo rendering processes we often wish to determine whether images being rendered are of sufficient visual quality, without the availability of a ground truth image. In such cases FR-IQA metrics are not applicable and we instead must utilise No-Reference Image Quality Assessment (NR-IQA) measures to make predictions about the perceived quality of unconverged images. In this work we propose a deep learning approach to NR-IQA, trained specifically on noise from Monte Carlo rendering processes, which significantly outperforms existing NR-IQA methods and can produce quality predictions consistent with FR-IQA measures that have access to ground truth images.en_US
dc.description.sectionheadersVision and Learning
dc.description.seriesinformationComputer Graphics and Visual Computing (CGVC)
dc.identifier.doi10.2312/cgvc.20181204
dc.identifier.isbn978-3-03868-071-0
dc.identifier.pages23-31
dc.identifier.urihttps://diglib.eg.org:443/handle/10.2312/cgvc20181204
dc.identifier.urihttps://doi.org/10.2312/cgvc.20181204
dc.publisherThe Eurographics Associationen_US
dc.subjectComputing methodologies
dc.subjectMachine learning
dc.subjectNeural networks
dc.subjectComputer graphics
dc.subjectImage processing
dc.titleA Deep Learning Approach to No-Reference Image Quality Assessment For Monte Carlo Rendered Imagesen_US
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