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

作　　者：Wen-Han Zhu Wei Sun Xiong-Kuo Min Guang-Tao Zhai Xiao-Kang Yang 

机构地区：[1]MoE Key Lab of Artificial Intelligence,AI Institute,Shanghai Jiao Tong University,Shanghai 200240,China [2]Institute of Image Communication and Information Processing,Shanghai Jiao Tong University,Shanghai 200240,China

出　　处：《International Journal of Automation and computing》2021年第2期204-218,共15页国际自动化与计算杂志（英文版）

基　　金：This work was supported by National Natural Science Foundation of China(Nos.61831015 and 61901260);Key Research and Development Program of China(No.2019YFB1405902).

摘　　要：Objective image quality assessment(IQA)plays an important role in various visual communication systems,which can automatically and efficiently predict the perceived quality of images.The human eye is the ultimate evaluator for visual experience,thus the modeling of human visual system(HVS)is a core issue for objective IQA and visual experience optimization.The traditional model based on black box fitting has low interpretability and it is difficult to guide the experience optimization effectively,while the model based on physiological simulation is hard to integrate into practical visual communication services due to its high computational complexity.For bridging the gap between signal distortion and visual experience,in this paper,we propose a novel perceptual no-reference(NR)IQA algorithm based on structural computational modeling of HVS.According to the mechanism of the human brain,we divide the visual signal processing into a low-level visual layer,a middle-level visual layer and a high-level visual layer,which conduct pixel information processing,primitive information processing and global image information processing,respectively.The natural scene statistics(NSS)based features,deep features and free-energy based features are extracted from these three layers.The support vector regression(SVR)is employed to aggregate features to the final quality prediction.Extensive experimental comparisons on three widely used benchmark IQA databases(LIVE,CSIQ and TID2013)demonstrate that our proposed metric is highly competitive with or outperforms the state-of-the-art NR IQA measures.

关 键 词：Image quality assessment(IQA) no-reference(NR) structural computational modeling human visual system visual feature extraction 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]