Image similarity metric (ISIM): a reduced reference image quality assessment approach

2015 ◽  
Vol 3 (1) ◽  
pp. 1-11 ◽  
Author(s):  
Vikrant Bhateja ◽  
Aseem Kalsi ◽  
Aastha Srivastava
Keyword(s):  
Image Quality ◽  
Quality Assessment ◽  
Image Quality Assessment ◽  
Image Similarity ◽  
Reference Image ◽  
Similarity Metric ◽  
Assessment Approach

Non reference image quality assessment approach based on standard model features of biological vision

2014 ◽  
Vol 29 (6) ◽  
pp. 1016-1023
Author(s):  
杨亚威 YANG Yawei ◽  
李俊山 LI Junshan ◽  
张士杰 ZHANG Shijie ◽  
芦鸿雁 LU Hongyan ◽  
胡双演 HU Shuangyan
Keyword(s):  
Image Quality ◽  
Standard Model ◽  
Quality Assessment ◽  
Image Quality Assessment ◽  
Reference Image ◽  
Biological Vision ◽  
Assessment Approach ◽  
Model Features

Image Quality Assessment Method Based on Fractional Fourier Transform

2013 ◽  
Vol 433-435 ◽  
pp. 372-375
Author(s):  
Bin Wang
Keyword(s):  
Fourier Transform ◽  
Image Quality ◽  
Quality Assessment ◽  
Image Quality Assessment ◽  
Fractional Fourier Transform ◽  
Quality Score ◽  
Reference Image ◽  
Support Vector ◽  
Two Dimensional ◽  
Assessment Approach

Image quality assessment is an important issue in the area of image processing, and the no-reference image quality assessment tries to evaluate the quality of image without the reference image. The present no-reference image quality assessment approach can not predict the quality score accurately. This paper proposes a new image quality assessment approach based on two-dimensional discrete fractional Fourier transform (FRFT). After the image is processed by two dimensional discrete fourier transform, the histogram of FRFT coefficients in different order are modeled by generalized Gaussian distribution (GGD). The parameters of GGD are estimated and the feature vector is formed by parameters of GGD. After that, the image is classified into five distortion type by the trained support vector machine. At last, the quality score is predicted by the trained support vector regression machine. The experiment results show that the performance of proposed method is better than the traditional method.

Tetrolet-based reduced reference image quality assessment approach

2014 ◽  
Author(s):  
Abdelkaher Ait Abdelouahad ◽  
Mounir Omari ◽  
Hocine Cherifi ◽  
Brahim Alibouch ◽  
Mohammed El Hassouni
Keyword(s):  
Image Quality ◽  
Quality Assessment ◽  
Image Quality Assessment ◽  
Reference Image ◽  
Assessment Approach

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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