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Proceedings Paper

Maximizing inpainting efficiency without sacrificing quality
Author(s): Paul A. Ardis; Christopher M. Brown
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Paper 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.

Paper Details

Date Published: 18 January 2010
PDF: 12 pages
Proc. SPIE 7529, Image Quality and System Performance VII, 75290U (18 January 2010); doi: 10.1117/12.837772
Show Author Affiliations
Paul A. Ardis, Univ. of Rochester (United States)
Christopher M. Brown, Univ. of Rochester (United States)

Published in SPIE Proceedings Vol. 7529:
Image Quality and System Performance VII
Susan P. Farnand; Frans Gaykema, Editor(s)

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