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Structured Computational Modeling of Human Visual System for No-reference Image Quality Assessment
Wen-Han Zhu1,2; Wei Sun2; Xiong-Kuo Min2; Guang-Tao Zhai2; Xiao-Kang Yang1
Source PublicationInternational Journal of Automation and Computing
ISSN1476-8186
2021
Volume18Issue:2Pages:204-218
Abstract

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.

KeywordImage quality assessment (IQA) no-reference (NR) structural computational modeling human visual system visual feature extraction
DOI10.1007/s11633-020-1270-z
Sub direction classification其他
planning direction of the national heavy laboratory其他
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Cited Times:2[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/44017
Collection学术期刊_Machine Intelligence Research
Affiliation1.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
Recommended Citation
GB/T 7714
Wen-Han Zhu,Wei Sun,Xiong-Kuo Min,et al. Structured Computational Modeling of Human Visual System for No-reference Image Quality Assessment[J]. International Journal of Automation and Computing,2021,18(2):204-218.
APA Wen-Han Zhu,Wei Sun,Xiong-Kuo Min,Guang-Tao Zhai,&Xiao-Kang Yang.(2021).Structured Computational Modeling of Human Visual System for No-reference Image Quality Assessment.International Journal of Automation and Computing,18(2),204-218.
MLA Wen-Han Zhu,et al."Structured Computational Modeling of Human Visual System for No-reference Image Quality Assessment".International Journal of Automation and Computing 18.2(2021):204-218.
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