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Alan C. Bovik
Researcher at University of Texas at Austin
Publications - 872
Citations - 120104
Alan C. Bovik is an academic researcher from University of Texas at Austin. The author has contributed to research in topics: Image quality & Video quality. The author has an hindex of 102, co-authored 837 publications receiving 96088 citations. Previous affiliations of Alan C. Bovik include University of Illinois at Urbana–Champaign & University of Sydney.
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A Subjective and Objective Study of Space-Time Subsampled Video Quality
Dae Yeol Lee,Somdyuti Paul,Christos G. Bampis,Hyunsuk Ko,Jongho Kim,Se Yoon Jeong,Blake Homan,Alan C. Bovik +7 more
TL;DR: The ETRI-LIVE Space-Time Sub-sampled Video Quality (ETRI-Live STSVQ) dataset as discussed by the authors contains 437 videos generated by applying various levels of combined space-time subsampling and video compression on 15 diverse video contents.
Proceedings ArticleDOI
Referenceless perceptual image defogging
TL;DR: The proposed defog and visibility enhancer makes use of statistical regularities observed in foggy and fog-free images to extract the most visible information from three processed image results: one white balanced and two contrast enhanced images.
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Quality Assessment of Perceptual Crosstalk on Two-View Auto-Stereoscopic Displays
TL;DR: The Binocular Perceptual Crosstalk Predictor (BPCP) uses measurements of three complementary 3D image properties in combination with two well-known factors to make predictions of crosstalk on two-view auto-stereoscopic displays and explores a new masking phenomenon that is called duplicated structure masking, which arises from structural correlations between the original and distorted objects.
Journal ArticleDOI
Blind Picture Upscaling Ratio Prediction
TL;DR: An accurate model for predicting the upscaling ratio applied to any natural image is developed by decomposing an input image frame using an orthogonal filter bank and locally normalizing the resulting responses, and it is shown that the local energy terms can be used to predict the upScaling ratio.
Journal ArticleDOI
Quality Prediction on Deep Generative Images
TL;DR: This work proposes a new “naturalness”-based image quality predictor for generative images that is built using a multi-stage parallel boosting system based on structural similarity features and measurements of statistical similarity.