1 January 2010 Inpainting quality assessment
Paul A. Ardis, Christopher M. Brown, Amit Singhal
Author Affiliations +
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)
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
Lens.org Logo
CITATIONS
Cited by 17 scholarly publications and 1 patent.
Advertisement
Advertisement
RIGHTS & PERMISSIONS
Get copyright permission  Get copyright permission on Copyright Marketplace
KEYWORDS
Image quality

Digital imaging

Image filtering

Image classification

Visualization

Optical filters

Image segmentation

Back to Top