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Journal of Electronic Imaging

Inpainting quality assessment
Author(s): Paul A. Ardis; Christopher M. Brown; Amit Singhal
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Paper Abstract

We propose a means of objectively comparing the results of digital image inpainting algorithms by analyzing changes in predicted human attention prior to and following application. Artifacting is generalized in two catagories, in-region and out-region, depending on whether or not attention changes are primarily within the edited region or in nearby (contrasting) regions. Human qualitative scores are shown to correlate strongly with numerical scores of in-region and out-region artifacting, including the effectiveness of training supervised classifiers of increasing complexity. Results are shown on two novel human-scored datasets.

Paper Details

Date Published: 1 January 2010
PDF: 7 pages
J. Electron. Imag. 19(1) 011002 doi: 10.1117/1.3267088
Published in: Journal of Electronic Imaging Volume 19, Issue 1
Show Author Affiliations
Paul A. Ardis, Univ. of Rochester (United States)
Christopher M. Brown, Univ. of Rochester (United States)
Amit Singhal, Eastman Kodak Co. (United States)

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