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
Predicting the Quality of Images Compressed After Distortion in Two Steps
TL;DR: A novel two-step image quality prediction concept that combines NR with R quality measurements, which is versatile as it can use any desired R and NR components, and constructed a new, first-of-a-kind dedicated database specialized for the design and testing of two- step IQA models.
Journal ArticleDOI
On eigenstructure-based direct multichannel blind image restoration
Hung-Ta Pai,Alan C. Bovik +1 more
TL;DR: The nullspace-based approach can be formulated as an optimization problem and it is shown that this formulation implies a new subspace- based approach that uses matrix operations that has the same advantages as the null space-based one but requires less computational complexity.
Journal ArticleDOI
Stereoscopic 3D Visual Discomfort Prediction: A Dynamic Accommodation and Vergence Interaction Model
TL;DR: A dynamic accommodation and vergence interaction (DAVI) model is developed that successfully predicts visual discomfort on S3D images and is based on the phasic and reflex responses of the fast fusional vergence mechanism.
Journal ArticleDOI
Survey of information theory in visual quality assessment
TL;DR: This work surveys information theoretic approaches to solve a variety of visual quality assessment (QA) problems and demonstrates the application of information theory in each of these problems, leading to different algorithms under different scenarios.
Proceedings ArticleDOI
Motion-based perceptual quality assessment of video
TL;DR: The MOtion-based Video Integrity Evaluation (MOVIE) as mentioned in this paper is an objective, full reference video quality index that integrates both spatial and temporal aspects of distortion assessment, and is shown to be competitive with, and even out-perform, existing video quality assessment systems.