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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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Proceedings Article

Texas active vision testbed

TL;DR: The Texas active vision testbed (TAVT) is used in the development of depth recovery algorithms to extend conventional active vision techniques, such as variable baseline stereo and active focus control, as well as support research in the active control of a vergent stereo geometry.
Patent

Detecting and correcting whiteboard images while enabling the removal of the speaker

TL;DR: In this paper, a video frame containing a whiteboard image is converted into a black and white image for the detection of boundaries, where boundaries are classified as horizontal or vertical lines and quadrangles are formed using spatial arrangements of these lines.
Proceedings ArticleDOI

Visual pattern image sequence coding

TL;DR: The algorithm for image sequence coding presented here termed Visual Pattern Image Sequence Coding (or VPISC) exploits all of the advantages of " static" VPIC in the reduction of information from an additional (temporal) dimension to achieve unprecedented image sequences coding performance stated in terms of coding complexity compression and visual fidelity.
Journal ArticleDOI

Making Video Quality Assessment Models Robust to Bit Depth

TL;DR: In this article , a feature set called HDRMAX features is introduced, which when included into Video Quality Assessment (VQA) algorithms designed for Standard Dynamic Range (SDR) videos, sensitizes them to distortions of High Dynamic Range videos that are inadequately accounted for by these algorithms.
Journal ArticleDOI

Perceptual Monocular Depth Estimation

TL;DR: In this article, a novel approach that uses perceptually relevant natural scene statistics (NSS) features to predict depths from monocular images in a simple, scale-agnostic way that is competitive with state-of-the-art systems is proposed.