Image quality assessment method based on human visual system

2018 ◽  
Author(s):  
Miao Zhu ◽  
Fan Wang ◽  
Jinping Ni ◽  
Rongli Guo
Keyword(s):  
Image Quality ◽  
Visual System ◽  
Quality Assessment ◽  
Human Visual System ◽  
Image Quality Assessment ◽  
Assessment Method ◽  
Quality Assessment Method

A Non-Contact Finger Vein Image Quality Assessment Method

2012 ◽  
Vol 239-240 ◽  
pp. 986-989
Author(s):  
Hui Ma ◽  
Feng Peng Cui ◽  
Popoola Oluwatoyin P
Keyword(s):  
Image Quality ◽  
Visual System ◽  
Quality Assessment ◽  
Human Visual System ◽  
Near Infrared ◽  
Image Quality Assessment ◽  
Effective Area ◽  
Assessment Method ◽  
Finger Vein ◽  
Quality Assessment Method

A finger vein image quality assessment method is based on both the human visual characteristics and finger vein image characteristics captured by the use of contactless and near infrared rays. We present an HSNR (signal to Noise ratio based on human visual system) finger vein image quality evaluation index by simulating the human visual system, and integrating the HSNR with effective area score, finger shifting score and contrast score to obtain the total image quality score of the finger vein image. Experimental results demonstrate that the proposed method is consistent with subjective assessment by humans, and thus can be used to describe the visual perception of the image effectively.

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

2021 ◽  
Author(s):  
Wen-Han Zhu ◽  
Wei Sun ◽  
Xiong-Kuo Min ◽  
Guang-Tao Zhai ◽  
Xiao-Kang Yang
Keyword(s):  
Information Processing ◽  
Image Quality ◽  
Visual System ◽  
Computational Modeling ◽  
Quality Assessment ◽  
Human Visual System ◽  
Visual Communication ◽  
Visual Experience ◽  
Image Quality Assessment ◽  
Model Based

AbstractObjective 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.

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