Non-reference quality assessment method based on occlusion region information estimation and natural scene statistics

SiQi Lv; LiJun Ren; YiQiang Zhang; ChengYang Huang; OuYang Yan; Jia He; Jing Hu

doi:10.1117/12.2678618

14 April 2023 Non-reference quality assessment method based on occlusion region information estimation and natural scene statistics

SiQi Lv, LiJun Ren, YiQiang Zhang, ChengYang Huang, OuYang Yan, Jia He, Jing Hu

Author Affiliations +

Proceedings Volume 12634, International Conference on Optics and Machine Vision (ICOMV 2023); 126340G (2023) https://doi.org/10.1117/12.2678618
Event: International Conference on Optics and Machine Vision (ICOMV 2023), 2023, Changsha, China

Abstract

Most of the laser interfered image quality assessment algorithms need to know the reference images or partial information of reference images. However, in practical application, the reference image or its related information is difficult to obtain, which makes the application scenario of laser interference image quality evaluation algorithm is greatly limited. To solve this problem, this paper starts with the prediction processing of the obscured information and improves the Markov Random Field estimation algorithm (MRF) to realize the real-time estimation of the obscured area information. Then, proposes a non-reference image quality assessment method based on occlusion area information estimation and natural scene statistics (IENSS), which analyzes the statistical characteristics of laser interfered images in natural scenes. The model is trained by machine learning. Finally, simulation experiments are carried out to verify the effectiveness of the proposed method.

Citation Download Citation

SiQi Lv, LiJun Ren, YiQiang Zhang, ChengYang Huang, OuYang Yan, Jia He, and Jing Hu "Non-reference quality assessment method based on occlusion region information estimation and natural scene statistics", Proc. SPIE 12634, International Conference on Optics and Machine Vision (ICOMV 2023), 126340G (14 April 2023); https://doi.org/10.1117/12.2678618

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