  Numerous single-image super-resolution algorithms have been proposed in the
literature, but few studies address the problem of performance evaluation based
on visual perception. While most super-resolution images are evaluated by
fullreference metrics, the effectiveness is not clear and the required
ground-truth images are not always available in practice. To address these
problems, we conduct human subject studies using a large set of
super-resolution images and propose a no-reference metric learned from visual
perceptual scores. Specifically, we design three types of low-level statistical
features in both spatial and frequency domains to quantify super-resolved
artifacts, and learn a two-stage regression model to predict the quality scores
of super-resolution images without referring to ground-truth images. Extensive
experimental results show that the proposed metric is effective and efficient
to assess the quality of super-resolution images based on human perception.

Computer Vision and Pattern Recognition