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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
A Local Flatness Based Variational Approach to Retinex
TL;DR: The experimental results show that the new approach to digitally implementing the Retinex using a local deviation based variational model can reconstruct more accurate recovered images than other state-of-the-art methods, while maintaining good contrast.
Journal ArticleDOI
Foveation-Based Error Resilience and Unequal Error Protection over Mobile Networks
TL;DR: Foveation-based error resilience and unequal error protection techniques over highly error-prone mobile networks are introduced and a foveation based bitstream partitioning is developed in order to alleviate the degradation of visual quality.
Journal ArticleDOI
Perceptually Scalable Extension of H.264
TL;DR: The non-uniform sampling characteristic of the human eye is used to modify scalable video coding (SVC) H.264/AVC and shows that the proposed VSVC framework significantly improves the subjective visual quality of compressed videos.
Proceedings ArticleDOI
Stereo disparity from multiscale processing of local image phase
Tieh-Yuh Chen,Alan C. Bovik +1 more
TL;DR: A stereo algorithm employing new modulation (multicomponent AM-FM) models for image representation, a disparity channel model for depth computation, and a multichannel processing for multi scale computation of stereo disparity from local image phase is presented.
Proceedings ArticleDOI
Extended depth-of-field using adjacent plane deblurring and MPP wavelet fusion for microscope images
TL;DR: This work introduces a novel method of extending the depth-of-field by fusing several optical sections in the wavelet domain using multiscale point-wise product (MPP) criteria and indicates both qualitatively and quantitatively that it outperforms existing schemes in the literature.