Eye gaze analysis and learning-to-rank to obtain the most preferred result in image inpainting

This paper proposes a method that blindly predicts preference order between inpainted images, aiming at selecting the best one from a plurality of results. Image inpainting, which removes unwanted regions and restores them, has attracted recent attention. However, it is known that the inpainting result varies largely with the method used for inpainting and the parameters set. Thus, in a typical use case, users need to manually select the inpainting method and the parameter that yields the best one. This manual selection takes a great deal of time and thus there is a great need for a way to automatically estimate the best result. Although some methods, such as estimating perceptual preference score from image features, have been proposed in recent years, none of them are considered very promising approaches. Our method focuses on the following two points: (1) what we essentially need is a preference order relation rather than an absolute score, and (2) we consider that image features for order estimation can be effectively designed by using actually measured human visual attention. Comparison with other image quality assessment methods shows that our method estimates the preference order with high accuracy.