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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
Massive Online Crowdsourced Study of Subjective and Objective Picture Quality
Deepti Ghadiyaram,Alan C. Bovik +1 more
TL;DR: The LIVE In the Wild Image Quality Challenge Database as discussed by the authors contains widely diverse authentic image distortions on a large number of images captured using a representative variety of modern mobile devices and has been used to conduct a very large-scale, multi-month image quality assessment subjective study.
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Automatic Prediction of Perceptual Image and Video Quality
TL;DR: The principles and methods of modern algorithms for automatically predicting the quality of visual signals are discussed and divided into understandable modeling subproblems by casting the problem as analogous to assessing the efficacy of a visual communication system.
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The Effect of Median Filtering on Edge Estimation and Detection
TL;DR: Noise images prefiltered by median filters defined with a variety of windowing geometries are used to support the analysis and it is found that median prefiltering improves the performance of both thresholding and zero-crossing based edge detectors.
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Perceptual quality prediction on authentically distorted images using a bag of features approach
Deepti Ghadiyaram,Alan C. Bovik +1 more
TL;DR: In this article, a bag-of-features approach is proposed to capture consistencies or departures therefrom of the statistics of real-world images in different color spaces and transform domains.
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AM-FM energy detection and separation in noise using multiband energy operators
TL;DR: It is demonstrated that the performance of the energy operator/ESA approach is vastly improved if the signal is first filtered through a bank of bandpass filters, and at each instant analyzed using the dominant local channel response.