Inpainting quality assessment

Paul A. Ardis; Christopher M. Brown; Amit Singhal

doi:10.1117/1.3267088

1 January 2010 Inpainting quality assessment

Paul A. Ardis, Christopher M. Brown, Amit Singhal

Author Affiliations +

Journal of Electronic Imaging, Vol. 19, Issue 1, 011002 (January 2010). https://doi.org/10.1117/1.3267088

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.

©(2010) Society of Photo-Optical Instrumentation Engineers (SPIE)

Citation Download Citation

Paul A. Ardis, Christopher M. Brown, and Amit Singhal "Inpainting quality assessment," Journal of Electronic Imaging 19(1), 011002 (1 January 2010). https://doi.org/10.1117/1.3267088

Published: 1 January 2010

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