scholarly journals On the performance of human visual system based image quality assessment metric using wavelet domain

2008 ◽  
Author(s):  
A. Ninassi ◽  
O. Le Meur ◽  
P. Le Callet ◽  
D. Barba
Keyword(s):  
Image Quality ◽  
Visual System ◽  
Quality Assessment ◽  
Human Visual System ◽  
Image Quality Assessment ◽  
Wavelet Domain

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.

Image quality assessment with a Gabor pyramid model of the human visual system

1997 ◽  
Author(s):  
Christopher C. Taylor ◽  
Zygmunt Pizlo ◽  
Jan P. Allebach ◽  
Charles A. Bouman
Keyword(s):  
Image Quality ◽  
Visual System ◽  
Quality Assessment ◽  
Human Visual System ◽  
Image Quality Assessment ◽  
Pyramid Model

scholarly journals Total Variation Based Perceptual Image Quality Assessment Modeling

2014 ◽  
Vol 2014 ◽  
pp. 1-10
Author(s):  
Yadong Wu ◽  
Hongying Zhang ◽  
Ran Duan
Keyword(s):  
Image Quality ◽  
Visual System ◽  
Quality Assessment ◽  
Total Variation ◽  
Human Visual System ◽  
Image Quality Assessment ◽  
Reference Image ◽  
Distorted Image ◽  
Perceptual Image Quality ◽  
Tv Model

Visual quality measure is one of the fundamental and important issues to numerous applications of image and video processing. In this paper, based on the assumption that human visual system is sensitive to image structures (edges) and image local luminance (light stimulation), we propose a new perceptual image quality assessment (PIQA) measure based on total variation (TV) model (TVPIQA) in spatial domain. The proposed measure compares TVs between a distorted image and its reference image to represent the loss of image structural information. Because of the good performance of TV model in describing edges, the proposed TVPIQA measure can illustrate image structure information very well. In addition, the energy of enclosed regions in a difference image between the reference image and its distorted image is used to measure the missing luminance information which is sensitive to human visual system. Finally, we validate the performance of TVPIQA measure with Cornell-A57, IVC, TID2008, and CSIQ databases and show that TVPIQA measure outperforms recent state-of-the-art image quality assessment measures.

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