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18 January 2010 Maximizing inpainting efficiency without sacrificing quality
Paul A. Ardis, Christopher M. Brown
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Proceedings Volume 7529, Image Quality and System Performance VII; 75290U (2010) https://doi.org/10.1117/12.837772
Event: IS&T/SPIE Electronic Imaging, 2010, San Jose, California, United States
Abstract
We propose a quality-aware computational optimization of inpainting based upon the intelligent application of a battery of inpainting methods. By leveraging the Decision-Action-Reward Network (DARN) formalism and a bottom-up model of human visual attention, methods are selected for optimal local use via an adjustable quality-time tradeoff and (empirical) training statistics aimed at minimizing observer foveal attention to inpainted regions. Results are shown for object removal in high-resolution consumer video, including a comparison of output quality and efficiency with homogeneous inpainting applications.
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Paul A. Ardis and Christopher M. Brown "Maximizing inpainting efficiency without sacrificing quality", Proc. SPIE 7529, Image Quality and System Performance VII, 75290U (18 January 2010); https://doi.org/10.1117/12.837772
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KEYWORDS
Video
Composites
Visualization
Image processing
Image quality
Visual process modeling
Data modeling
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