Subjective and objective evaluation of image inpainting quality

Image inpainting algorithms aim to cut out parts of the image without leaving holes. Various algorithms exist, but no wider comparison has been made, yet. This work fills the gap by comparing state-of-the-art algorithms in a user study. We create and publish a database consisting of multiple base images and inpaint them using different inpainting concepts. Afterwards, 21 participants are asked to rate the quality of these inpainted images. The subjective feedback indicates that different image inpainting algorithms are favorable depending on the characteristics of the base image and target region. Furthermore, the results show that general image quality measures such as the peak signal-to-noise ratio (PSNR) or the structural similarity (SSIM) index are not suited for judging inpainting quality.