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https://hdl.handle.net/2440/117265
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Type: | Journal article |
Title: | Learning a no-reference quality metric for single-image super-resolution |
Author: | Ma, C. Yang, C.-Y. Yang, X. Yang, M.-H. |
Citation: | Computer Vision and Image Understanding, 2017; 158:1-16 |
Publisher: | Elsevier |
Issue Date: | 2017 |
ISSN: | 1077-3142 1090-235X |
Statement of Responsibility: | Chao Ma, Chih-Yuan Yang, Xiaokang Yang, Ming-Hsuan Yang |
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. |
Keywords: | Image quality assessment; no-reference metric; single-image super-resolution |
Rights: | © 2017 Elsevier Inc. All rights reserved. |
DOI: | 10.1016/j.cviu.2016.12.009 |
Published version: | http://dx.doi.org/10.1016/j.cviu.2016.12.009 |
Appears in Collections: | Aurora harvest 8 Computer Science publications |
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