Structured Computational Modeling of Human Visual System for No-reference Image Quality Assessment

Wen-Han Zhu, Wei Sun, Xiong-Kuo Min, Guang-Tao Zhai, Xiao-Kang Yang. Structured Computational Modeling of Human Visual System for No-reference Image Quality Assessment. International Journal of Automation and Computing. doi: 10.1007/s11633-020-1270-z
 Citation: Wen-Han Zhu, Wei Sun, Xiong-Kuo Min, Guang-Tao Zhai, Xiao-Kang Yang. Structured Computational Modeling of Human Visual System for No-reference Image Quality Assessment. International Journal of Automation and Computing.

## Structured Computational Modeling of Human Visual System for No-reference Image Quality Assessment

###### Author Bio: Wen-Han Zhu received the B. Eng. degree in electronic information engineering from Huazhong University of Science and Technology, China in 2015. He is currently a Ph. D. degree candidate in information and communication engineering with Institute of Image Communication and Network Engineering, Shanghai Jiao Tong University, China. His research interests include image quality assessment and image processing. E-mail: zhuwenhan823@sjtu.edu.cnORCID iD: 0000-0001-8781-1110 Wei Sun received the B. Eng. degree in automation from East China University of Science and Technology, China in 2016. He is currently a Ph. D. degree candidate in control science and engineering at Institute of Image Communication and Network Engineering, Shanghai Jiao Tong University, China. His research interests include image quality assessment, perceptual signal processing and mobile video processing. E-mail: sunguwei@sjtu.edu.cn Xiong-Kuo Min received the B. Eng. degree in electronic and information engineering from Wuhan University, China in 2013, and the Ph. D. degree in information and communication engineering from Shanghai Jiao Tong University, China in 2018. From January 2016 to January 2017, he was a visiting student at Department of Electrical and Computer Engineering, University of Waterloo, Canada. He is currently a post-doctoral fellow with Shanghai Jiao Tong University. He received the Best Student Paper Award at IEEE ICME 2016. His research interests include visual quality assessment, visual attention modeling and perceptual signal processing. E-mail: minxiongkuo@sjtu.edu.cn Guang-Tao Zhai received the B. Eng. degree in information science and engineering and M. Eng. degree in information science and engineering from Shandong University, China in 2001 and 2004, respectively, and the Ph. D. degree in communication and information system from Shanghai Jiao Tong University, China in 2009, where he is currently a research professor with Institute of Image Communication and Information Processing. From 2008 to 2009, he was a visiting student with Department of Electrical and Computer Engineering, McMaster University, Canada, where he was a post-doctoral fellow from 2010 to 2012. From 2012 to 2013, he was a Humboldt Research Fellow with Institute of Multimedia Communication and Signal Processing, Friedrich Alexander University of Erlangen-Nuremberg, Germany. He received the Award of National Excellent Ph.D. Thesis from the Ministry of Education of China in 2012. His research interests include multimedia signal processing and perceptual signal processing. E-mail: zhaiguangtao@sjtu.edu.cn (Corresponding author)ORCID iD: 0000-0001-8165-9322 Xiao-Kang Yang received the B. Sc. degree in physics from Xiamen University, China in 1994, the M. Sc. degree in physics from Chinese Academy of Sciences, China in 1997, and the Ph. D. degree in pattern recognition and intelligent system from Shanghai Jiao Tong University, China in 2000. He is currently a Distinguished Professor with the School of Electronic Information and Electrical Engineering, and the Deputy Director of the Institute of Image Communication and Information Processing, Shanghai Jiao Tong University, China. From 2000 to 2002, he was a Research Fellow with the Centre for Signal Processing, Nanyang Technological University, Singapore. From 2002 to 2004, he was a Research Scientist with the Institute for Infocomm Research, Singapore. From 2007 to 2008, he visited Institute for Computer Science, University of Freiburg, Germany, as an Alexander von Humboldt Research Fellow. He has published over 200 refereed papers, and has filed 60 patents. He is an Associate Editor of IEEE Transactions on Multimedia and a Senior Associate Editor of IEEE Signal Processing Letters. He was a Series Editor of Springer CCIS, and an Editorial Board Member of Digital Signal Processing. He is a member of Asia-Pacific Signal and Information Processing Association, the VSPC Technical Committee of the IEEE Circuits and Systems Society, and the MMSP Technical Committee of the IEEE Signal Processing Society. He is also Chair of the Multimedia Big Data Interest Group of MMTC Technical Committee, IEEE Communication Society. His research interests include image processing and communication, computer vision, and machine learning. E-mail: xkyang@sjtu.edu.cn
• Figure  1.  Diagram of our proposed structural computational modeling of human visual system

Figure  2.  Overall diagram of the NSCHM algorithm

Figure  3.  MSCN distributions of a reference image and its corresponding degraded versions with different distortion types including additive white Gaussian noise (AWGN), Gaussian blur (GBlur), JPEG compression (JPEG) and JPEG2000 (JP2K): (a) Original image extracted from TID2013 database; (b) MSCN distributions. Colored figures are available in the online version.

Figure  4.  Distributions of the products of the adjacent MSCN coefficients along the horizontal orientation of an original image and its corresponding degraded versions distorted by AWGN, GBlur, JPEG and JP2K.

Figure  5.  Framework of the proposed middle-level visual features extraction. ${{{P}}_1}$ to ${{{P}}_n}$ are multiple pseudo reference images, ${{{R}}_d}$ and ${{{R}}_1}$ to ${{{R}}_n}$ denote the representation maps in VGG. The VGG-based perceptual representation includes six stages, in which the zeroth stage is the raw pixels. ${{{M}}_1}$ to ${{{M}}_n}$ indicate the distorted image′s middle-level visual features combining the texture and structure features of target distorted image and its corresponding multiple pseudo reference images at different convolution layers.

Figure  6.  Relationship between high-level visual feature $H$ and distortion levels with different types. (a) and (b) are two reference images selected from the TID2013 database. (c) shows the visual feature $H$ of (a) over different distortion levels distorted by GBlur. (d) shows the visual feature $H$ of (b) over different distortion levels degraded by JPEG.

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## Structured Computational Modeling of Human Visual System for No-reference Image Quality Assessment

### English Abstract

Wen-Han Zhu, Wei Sun, Xiong-Kuo Min, Guang-Tao Zhai, Xiao-Kang Yang. Structured Computational Modeling of Human Visual System for No-reference Image Quality Assessment. International Journal of Automation and Computing. doi: 10.1007/s11633-020-1270-z
 Citation: Wen-Han Zhu, Wei Sun, Xiong-Kuo Min, Guang-Tao Zhai, Xiao-Kang Yang. Structured Computational Modeling of Human Visual System for No-reference Image Quality Assessment. International Journal of Automation and Computing.

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