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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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Journal ArticleDOI
Blind Image Quality Assessment: From Natural Scene Statistics to Perceptual Quality
Anush K. Moorthy,Alan C. Bovik +1 more
TL;DR: DIIVINE is capable of assessing the quality of a distorted image across multiple distortion categories, as against most NR IQA algorithms that are distortion-specific in nature, and is statistically superior to the often used measure of peak signal-to-noise ratio (PSNR) and statistically equivalent to the popular structural similarity index (SSIM).
Journal ArticleDOI
Blind Image Quality Assessment: A Natural Scene Statistics Approach in the DCT Domain
TL;DR: An efficient general-purpose blind/no-reference image quality assessment (IQA) algorithm using a natural scene statistics model of discrete cosine transform (DCT) coefficients, which requires minimal training and adopts a simple probabilistic model for score prediction.
Proceedings ArticleDOI
Image information and visual quality
Hamid R. Sheikh,Alan C. Bovik +1 more
TL;DR: This work proposes an information fidelity criterion that quantifies the Shannon information that is shared between the reference and distorted images relative to the information contained in the reference image itself, and demonstrates the performance of the algorithm by testing it on a data set of 779 images.
Journal ArticleDOI
An information fidelity criterion for image quality assessment using natural scene statistics
TL;DR: This paper proposes a novel information fidelity criterion that is based on natural scene statistics and derives a novel QA algorithm that provides clear advantages over the traditional approaches and outperforms current methods in testing.
Journal ArticleDOI
Gradient Magnitude Similarity Deviation: A Highly Efficient Perceptual Image Quality Index
TL;DR: It is found that the pixel-wise gradient magnitude similarity (GMS) between the reference and distorted images combined with a novel pooling strategy-the standard deviation of the GMS map-can predict accurately perceptual image quality.