Zhang, Yin, Xuehan, Bai, Yan, Junhua, Xiao, Yongqi, Zhang, Wanyi, Chatwin, Chris and Young, Rupert (2019) A full-reference image quality assessment for multiply distorted image based on visual mutual information. Journal of Imaging Science and Technology. ISSN 1062-3701

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Abstract

A full-reference image quality assessment (FR-IQA) method for multi-distortion based on visual mutual information (MD-IQA) is proposed to solve the problem that the existing FR-IQA methods are mostly applicable to single-distorted images, but the assessment result for multiply distorted images is not ideal. First, the reference image and the distorted image are preprocessed by steerable pyramid decomposition and contrast sensitivity function (CSF). Next, a Gaussian scale mixture (GSM) model and an image distorted model are respectively constructed for the reference images and the distorted images. Then, visual distorted models are constructed both for the reference images and the distorted images. Finally, the mutual information between the processed reference image and the distorted image is calculated to obtain the full-reference quality assessment index for multiply distorted images. The experimental results show that the proposed method has higher accuracy and better performance for multiply distorted images.

Item Type:	Article
Keywords:	image processing, distortion, Gaussian mixture, recognition, multiple distortions
Schools and Departments:	School of Engineering and Informatics > Engineering and Design
Research Centres and Groups:	Industrial Informatics and Signal Processing Research Group
Subjects:	Q Science > QA Mathematics > QA0297 Numerical analysis Q Science > QA Mathematics > QA0075 Electronic computers. Computer science T Technology > TK Electrical engineering. Electronics Nuclear engineering > TK6680.5 Digital video. General works T Technology > TK Electrical engineering. Electronics Nuclear engineering > TK7800 Electronics > TK7874 Microelectronics. Integrated circuits
Depositing User:	Chris Chatwin
Date Deposited:	22 Jul 2019 14:50
Last Modified:	22 Jul 2019 15:17
URI:	http://sro.sussex.ac.uk/id/eprint/85011

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