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

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