Special Section on Image Quality

Inpainting quality assessment

[+] Author Affiliations
Paul A. Ardis

University of Rochester, Rochester, New York 14627

Christopher M. Brown

University of Rochester, Rochester, New York 14627

Amit Singhal

Eastman Kodak Company, Research Laboratories, Rochester, New York 14650-2103

J. Electron. Imaging. 19(1), 011002 (January 07, 2010). doi:10.1117/1.3267088
History: Received April 29, 2009; Revised June 29, 2009; Accepted July 21, 2009; Published January 07, 2010
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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.

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Citation

Paul A. Ardis ; Christopher M. Brown and Amit Singhal
"Inpainting quality assessment", J. Electron. Imaging. 19(1), 011002 (January 07, 2010). ; http://dx.doi.org/10.1117/1.3267088


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