Learning to Predict Localized Distortions in Rendered Images

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dc.contributor.authorCadík, Martinen_US
dc.contributor.authorHerzog, Roberten_US
dc.contributor.authorMantiuk, Rafalen_US
dc.contributor.authorMantiuk, Radoslawen_US
dc.contributor.authorMyszkowski, Karolen_US
dc.contributor.authorSeidel, Hans-Peteren_US
dc.contributor.editorB. Levy, X. Tong, and K. Yinen_US
dc.date.accessioned2015-02-28T16:13:07Z
dc.date.available2015-02-28T16:13:07Z
dc.date.issued2013en_US
dc.description.abstractIn this work, we present an analysis of feature descriptors for objective image quality assessment. We explore a large space of possible features including components of existing image quality metrics as well as many traditional computer vision and statistical features. Additionally, we propose new features motivated by human perception and we analyze visual saliency maps acquired using an eye tracker in our user experiments. The discriminative power of the features is assessed by means of a machine learning framework revealing the importance of each feature for image quality assessment task. Furthermore, we propose a new data-driven full-reference image quality metric which outperforms current state-of-the-art metrics. The metric was trained on subjective ground truth data combining two publicly available datasets. For the sake of completeness we create a new testing synthetic dataset including experimentally measured subjective distortion maps. Finally, using the same machine-learning framework we optimize the parameters of popular existing metrics.en_US
dc.description.seriesinformationComputer Graphics Forumen_US
dc.identifier.issn1467-8659en_US
dc.identifier.urihttps://doi.org/10.1111/cgf.12248en_US
dc.publisherThe Eurographics Association and Blackwell Publishing Ltd.en_US
dc.subjectI.3.3 [Computer Graphics]en_US
dc.subjectPicture/Image Generationen_US
dc.subjectImage Quality Assessmenten_US
dc.titleLearning to Predict Localized Distortions in Rendered Imagesen_US
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