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A Subjective Assessment Method of Aerospace Image Quality Based on Human Visual System

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Man–Machine–Environment System Engineering (MMESE 2019)

Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 576))

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Abstract

Aiming at the problems of blur, noise and contrast distortion in aerospace images, a subjective image quality assessment method based on human visual characteristics is proposed in this paper. First, the aerospace image database with different blur, noise and contrast distortions is simulated by 3D modelling software. Second, the subjects are required to give three image quality scores based on the indexes above, respectively. Third, the correlations of three indexes’ scores are calculated by the Pearson formula and then different weights are given to the indexes based on the calculated correlation coefficient above. Finally, the modified subjective image assessment scores can be obtained by the multi-factor model. The proposed subjective evaluation model takes into account the influence of multiple factors that can show the characteristics of images comprehensively. Many experiment results have verified the correctness of the proposed method.

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Acknowledgements

This work is supported by the National Natural Science Foundation of China under Grant No. 61501016 and the open project of the State Key Laboratory of Intense Pulsed Radiation Simulation and Effect under Grant No. SKLIPR1713.

Compliance with Ethical Standards

The study was approved by the Logistics Department for Civilian Ethics Committee of University of Science and Technology Beijing. All subjects who participated in the experiment were provided with and signed an informed consent form. All relevant ethical safeguards have been met with regard to subject protection.

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Correspondence to Wuyuan Zhou .

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Zhou, W. et al. (2020). A Subjective Assessment Method of Aerospace Image Quality Based on Human Visual System. In: Long, S., Dhillon, B. (eds) Man–Machine–Environment System Engineering . MMESE 2019. Lecture Notes in Electrical Engineering, vol 576. Springer, Singapore. https://doi.org/10.1007/978-981-13-8779-1_31

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