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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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Proceedings ArticleDOI
Halftoning-Inspired Methods for Foveation in Variable-Acuity Superpixel Imager Cameras
TL;DR: This work compares approaches for generating binary control signals for variable acuity superpixel imager (VASI™) cameras, and measures five objective figures of merit after foveating a small set of test images with a variety of halftoning-inspired approaches.
Proceedings ArticleDOI
Pik-Fix: Restoring and Colorizing Old Photos
TL;DR: A novel reference-based end-to-end learning framework that is able to both repair and colorize old, degraded pictures and outperforms previous state-of-the-art models using both qualitative comparisons and quantitative measurements is presented.
Proceedings ArticleDOI
Demodulation of AM-FM Signals in Noise Using Multiband Energy Operators
TL;DR: With time-varying amplitude a and instantaneous frequency o i = 4 , using the operator Y(s) = ($2-ss' developed by Teager [I] and Kaiser [2], shown to be highly effective for detecting AM-FM modulations.
Journal ArticleDOI
Disparity statistics in the natural environment
TL;DR: Yang et al. as mentioned in this paper determined the statistics of naturally occurring binocular disparities in outdoor environments to find out if they could potentially play a role in vision under these circumstances using a data base of range images obtained with a scanning laser rangefinder from a wooded environment.
Journal ArticleDOI
Video Quality Model of Compression, Resolution and Frame Rate Adaptation Based on Space-Time Regularities
TL;DR: A video quality predictor that is sensitive to spatial, temporal, or space-time subsampling combined with compression and achieves state-of-the-art (SOTA) prediction performance on the new ETRI-LIVE Space-Time Subsampled Video Quality (STSVQ) and also on the AVT-VQDB-UHD-1 database.