Learning No-reference Quality Metric for Single-Image Super-Resolution

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Authors Chao Ma, , ChihYuan Yang, Xiaokang Yang, Ming-Hsuan Yang
Journal/Conference Name COMPUTER VISION AND IMAGE UNDERSTANDING
Paper Category
Paper Abstract Abstract 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.
Date of publication 2017
Code Programming Language MATLAB
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