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 full-reference 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.
Bibliographical noteFunding Information:
This work is supported in part by The National Key Research and Development Program of China (2016YFB1001003), NSFC (61527804, 61521062), STCSM (14XD1402100) and the 111 Program (B07022).
© 2017 Elsevier Inc.
All Science Journal Classification (ASJC) codes
- Signal Processing
- Computer Vision and Pattern Recognition