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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.
Papers
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Book ChapterDOI
Image Quality Assessment
Kalpana Seshadrinathan,Thrasyvoulos N. Pappas,Robert J. Safranek,Junqing Chen,Zhou Wang,Hamid R. Sheikh,Alan C. Bovik +6 more
TL;DR: In this article, the authors examine objective criteria for the evaluation of image quality as perceived by an average human observer and highlight the similarities, dissimilarities, and interplay between these seemingly diverse techniques.
Proceedings ArticleDOI
Digital halftoning as 2-D delta-sigma modulation
TL;DR: The error diffusion algorithm for digital halftoning is equivalent in form to a noise-shaping feedback coder, a class of delta-sigma modulator, and a gain model for the quantizer is used to account for this correlation.
Posted Content
A Probabilistic Quality Representation Approach to Deep Blind Image Quality Prediction.
Hui Zeng,Lei Zhang,Alan C. Bovik +2 more
TL;DR: The proposed PQR method is shown to not only speed up the convergence of deep model training, but to also greatly improve the achievable level of quality prediction accuracy relative to scalar quality score regression methods.
Proceedings ArticleDOI
Skewed 2D Hilbert transforms and computed AM-FM models
TL;DR: This work applies the directional 2D Hilbert transform to compute a complex extension for a real image called the analytic image, which admits most of the attractive properties of the 1D analytic signal.
Journal ArticleDOI
No-reference image blur index based on singular value curve
TL;DR: Experimental results obtained show that the proposed SVC algorithm achieves high correlation against human judgments when assessing the blur distortion of images and is well-suited for real-time applications.