An improved image quality objective assessment method using non-linear calibration algorithm

2014 ◽  
Author(s):  
Shuang Yang ◽  
Fang Meng ◽  
Xiuhua Jiang ◽  
Hao Liu
Keyword(s):  
Image Quality ◽  
Objective Assessment ◽  
Assessment Method ◽  
Linear Calibration ◽  
Non Linear ◽  
Calibration Algorithm ◽  
Quality Objective

Study on Image Quality Assessment with Scale Space Approach Using Index of Visual Evoked Potentials

2011 ◽  
pp. 165-176
Author(s):  
Hidehiko Hayashi ◽  
Akinori Minazuki
Keyword(s):  
Image Quality ◽  
Evoked Potentials ◽  
Visual Evoked Potentials ◽  
Image Quality Assessment ◽  
Objective Assessment ◽  
Scale Space ◽  
Assessment Method ◽  
Pattern Reversal ◽  
Scalar Parameter ◽  
Visual Evoked

This chapter presents an objective assessment method of image quality using visual evoked potentials (VEPs) to image engineer field based on multi-disciplinarily approach such as knowledge of neurobiology, image recognition theory, or computer vision. The multi-disciplinarily based objective assessment method applies Gaussian scale-space filtering in order to define a scalar parameter to depict blur image. In the experiment, visual stimuli are provided by the scalar parameter, and subjects are detected using VEPs. Their VEPs are recoded during observation of the checkerboard pattern reversal (PR) stimuli, and are analyzed with a latency of about Negative 145 msec (N145) component. The result of the experiment was that latency of N145 components were long about10-20 msec when parameters were large vale (more blur). This result shows one example of availableness for the multi-disciplinarily based objective assessment of image quality by integrating the pattern reversal visual evoked potential (PR-VEP) and the scale-space theory.

Study on Image Quality Assessment with Scale Space Approach Using Index of Visual Evoked Potentials

2013 ◽  
pp. 927-938
Author(s):  
Hidehiko Hayashi ◽  
Akinori Minazuki
Keyword(s):  
Image Quality ◽  
Evoked Potentials ◽  
Visual Evoked Potentials ◽  
Image Quality Assessment ◽  
Objective Assessment ◽  
Scale Space ◽  
Assessment Method ◽  
Pattern Reversal ◽  
Scalar Parameter ◽  
Visual Evoked

This chapter presents an objective assessment method of image quality using visual evoked potentials (VEPs) to image engineer field based on multi-disciplinarily approach such as knowledge of neurobiology, image recognition theory, or computer vision. The multi-disciplinarily based objective assessment method applies Gaussian scale-space filtering in order to define a scalar parameter to depict blur image. In the experiment, visual stimuli are provided by the scalar parameter, and subjects are detected using VEPs. Their VEPs are recoded during observation of the checkerboard pattern reversal (PR) stimuli, and are analyzed with a latency of about Negative 145 msec (N145) component. The result of the experiment was that latency of N145 components were long about10-20 msec when parameters were large vale (more blur). This result shows one example of availableness for the multi-disciplinarily based objective assessment of image quality by integrating the pattern reversal visual evoked potential (PR-VEP) and the scale-space theory.

No-Reference Image Quality Assessment Based on Multi-Order Gradients Statistics

2020 ◽  
Vol 64 (1) ◽  
pp. 10505-1-10505-16
Author(s):  
Yin Zhang ◽  
Xuehan Bai ◽  
Junhua Yan ◽  
Yongqi Xiao ◽  
C. R. Chatwin ◽  
...  
Keyword(s):  
Neural Network ◽  
Image Quality ◽  
Quality Assessment ◽  
Image Quality Assessment ◽  
Assessment Method ◽  
Image Distortion ◽  
Image Feature ◽  
Reference Image ◽  
Gradient Magnitude ◽  
Distortion Type

Abstract A new blind image quality assessment method called No-Reference Image Quality Assessment Based on Multi-Order Gradients Statistics is proposed, which is aimed at solving the problem that the existing no-reference image quality assessment methods cannot determine the type of image distortion and that the quality evaluation has poor robustness for different types of distortion. In this article, an 18-dimensional image feature vector is constructed from gradient magnitude features, relative gradient orientation features, and relative gradient magnitude features over two scales and three orders on the basis of the relationship between multi-order gradient statistics and the type and degree of image distortion. The feature matrix and distortion types of known distorted images are used to train an AdaBoost_BP neural network to determine the image distortion type; the feature matrix and subjective scores of known distorted images are used to train an AdaBoost_BP neural network to determine the image distortion degree. A series of comparative experiments were carried out using Laboratory of Image and Video Engineering (LIVE), LIVE Multiply Distorted Image Quality, Tampere Image, and Optics Remote Sensing Image databases. Experimental results show that the proposed method has high distortion type judgment accuracy and that the quality score shows good subjective consistency and robustness for all types of distortion. The performance of the proposed method is not constricted to a particular database, and the proposed method has high operational efficiency.

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