18 January 2010 Maximizing inpainting efficiency without sacrificing quality
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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, Paul A. Ardis, Christopher M. Brown, Christopher M. Brown, } "Maximizing inpainting efficiency without sacrificing quality", Proc. SPIE 7529, Image Quality and System Performance VII, 75290U (18 January 2010); doi: 10.1117/12.837772; https://doi.org/10.1117/12.837772
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