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Article

Full-Reference Quality Metric Based on Neural Network to Assess the Visual Quality of Remote Sensing Images

1
Department of Information and Communication Technologies, National Aerospace University, 61070 Kharkov, Ukraine
2
Department of Signal Processing and Multimedia Engineering, West Pomeranian University of Technology in Szczecin, 70-313 Szczecin, Poland
3
Computational Imaging Group, Tampere University, 33720 Tampere, Finland
*
Author to whom correspondence should be addressed.
Remote Sens. 2020, 12(15), 2349; https://doi.org/10.3390/rs12152349
Received: 10 June 2020 / Revised: 10 July 2020 / Accepted: 20 July 2020 / Published: 22 July 2020
Remote sensing images are subject to different types of degradations. The visual quality of such images is important because their visual inspection and analysis are still widely used in practice. To characterize the visual quality of remote sensing images, the use of specialized visual quality metrics is desired. Although the attempts to create such metrics are limited, there is a great number of visual quality metrics designed for other applications. Our idea is that some of these metrics can be employed in remote sensing under the condition that those metrics have been designed for the same distortion types. Thus, image databases that contain images with types of distortions that are of interest should be looked for. It has been checked what known visual quality metrics perform well for images with such degradations and an opportunity to design neural network-based combined metrics with improved performance has been studied. It is shown that for such combined metrics, their Spearman correlation coefficient with mean opinion score exceeds 0.97 for subsets of images in the Tampere Image Database (TID2013). Since different types of elementary metric pre-processing and neural network design have been considered, it has been demonstrated that it is enough to have two hidden layers and about twenty inputs. Examples of using known and designed visual quality metrics in remote sensing are presented. View Full-Text
Keywords: image quality assessment; visual quality metrics; neural networks; combined metrics image quality assessment; visual quality metrics; neural networks; combined metrics
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MDPI and ACS Style

Ieremeiev, O.; Lukin, V.; Okarma, K.; Egiazarian, K. Full-Reference Quality Metric Based on Neural Network to Assess the Visual Quality of Remote Sensing Images. Remote Sens. 2020, 12, 2349. https://doi.org/10.3390/rs12152349

AMA Style

Ieremeiev O, Lukin V, Okarma K, Egiazarian K. Full-Reference Quality Metric Based on Neural Network to Assess the Visual Quality of Remote Sensing Images. Remote Sensing. 2020; 12(15):2349. https://doi.org/10.3390/rs12152349

Chicago/Turabian Style

Ieremeiev, Oleg, Vladimir Lukin, Krzysztof Okarma, and Karen Egiazarian. 2020. "Full-Reference Quality Metric Based on Neural Network to Assess the Visual Quality of Remote Sensing Images" Remote Sensing 12, no. 15: 2349. https://doi.org/10.3390/rs12152349

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