Reduced-reference quality assessment of image super-resolution by energy change and texture variation

• Main Authors: Fang, Yuming, Liu, Jiaying, Zhang, Yabin, Lin, Weisi, Guo, Zongming
• Other Authors: School of Computer Science and Engineering
• Format: Article
• Language: English
• Published: 2021
• Subjects: Engineering::Computer science and engineering; Image Quality Assessment; Image Super-resolution
• Online Access: https://hdl.handle.net/10356/147379
• Institution: Nanyang Technological University

In this paper, we propose a novel reduced-reference quality assessment metric for image super-resolution (RRIQA-SR) based on the low-resolution (LR) image information. With the pixel correspondence, we predict the perceptual similarity between image patches of LR and SR images by two components: the energy change in low-frequency regions, which can be used to capture the global distortion in SR images, and texture variation in high-frequency regions, which can be used to capture the local distortion in SR images. The overall quality of SR images is estimated by perceptual similarity calculated by energy change and texture variation between local image patches of LR and HR images. Experimental results demonstrate that the proposed method can obtain better performance of quality prediction for SR images than other existing ones, even including some full-reference (FR) metrics.