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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 ArticleDOI
An unequal error protection scheme for multiple input multiple output systems
TL;DR: An unequal error protection scheme based on the combined use of turbo codes and space-time codes for communication over wireless channels is proposed and shows a 25% reduction in average transmission time and a 15 dB improvement compared with no spatial diversity.
Proceedings ArticleDOI
Automated Facial Feature Detection from Portrait and Range Images
TL;DR: In this article, the appearance of each feature point is encoded using a set of Gabor wavelet responses extracted at multiple orientations and spatial frequencies, which are computed at each pixel in the search window on a fiducial.
Journal ArticleDOI
Optimizing Multiscale SSIM for Compression via MLDS
TL;DR: A recently developed method for assessing perceived image quality, maximum likelihood difference scaling (MLDS), is used and it is shown how the data collected by MLDS allow the performance of a widely-used image quality assessment algorithm, multiscale structural similarity (MS-SSIM), to improve.
Journal ArticleDOI
Cloud Detection in Satellite Images Based on Natural Scene Statistics and Gabor Features
TL;DR: A novel cloud detection algorithm for optical RS images, whereby test images are separated into three classes: thick clouds, thin clouds, and noncloudy, and a simple linear iterative clustering algorithm is adopted that is able to segment potential clouds, including small clouds.
Journal ArticleDOI
Feature-based prediction of streaming video QoE: Distortions, stalling and memory
Christos G. Bampis,Alan C. Bovik +1 more
TL;DR: A feature-based approach that combines a number of QoE-related features, including perceptually-relevant quality features, stalling-aware features and memory-driven features to makeQoE predictions, which provides improved performance over state-of-the-art video quality metrics while generalizing well on a different dataset is developed.