A
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
More filters
Journal ArticleDOI
Color as a source of information in the stereo correspondence process.
TL;DR: Using an ideal-observer analysis, it is found that chromatic cues were used much more efficiently than luminance cues in disambiguating these stereograms when the patterns were presented on a dark background but were used with about equal efficiency when presenting on a light background.
Journal ArticleDOI
UGC-VQA: Benchmarking Blind Video Quality Assessment for User Generated Content
TL;DR: In this article, the VIDeo quality EVALuator (VIDEVAL) is proposed to improve the performance of VQA models for UGC/consumer videos.
Journal ArticleDOI
Fast algorithms for foveated video processing
Sanghoon Lee,Alan C. Bovik +1 more
TL;DR: This approach leads to enhanced computational efficiency by interpreting nonuniform-density foveated images on the uniform domain and by using a foveation protocol between the encoder and the decoder.
Journal ArticleDOI
The reliability of measuring physical characteristics of spiculated masses on mammography
Mehul Sampat,Gary J. Whitman,Tanya W. Stephens,L. D. Broemeling,N. A. Heger,Alan C. Bovik,Mia K. Markey +6 more
TL;DR: Physical properties of spiculated masses can be measured reliably on mammography and the interobserver and intraobserver variability for this task is comparable with that reported for other measurements made on medical images.
Journal ArticleDOI
Analyzing Image Structure by Multidimensional Frequency Modulation
TL;DR: This work develops a mathematical framework for quantifying and understanding multidimensional frequency modulations in digital images and derives the ordinary differential equations (ODEs) that describe image flowlines.