No-reference image quality assessment using local binary pattern in the wavelet domain

2017 ◽  
Vol 77 (2) ◽  
pp. 2529-2541 ◽  
Author(s):  
Farshad Rezaie ◽  
Mohammad Sadegh Helfroush ◽  
Habibollah Danyali
Keyword(s):  
Image Quality ◽  
Quality Assessment ◽  
Local Binary Pattern ◽  
Image Quality Assessment ◽  
Reference Image ◽  
Wavelet Domain

Local binary pattern statistics feature for reduced reference image quality assessment

2013 ◽  
Author(s):  
Min Zhang ◽  
Xuanqin Mou ◽  
Hiroshi Fujita ◽  
Lei Zhang ◽  
Xiangrong Zhou ◽  
...  
Keyword(s):  
Image Quality ◽  
Quality Assessment ◽  
Local Binary Pattern ◽  
Image Quality Assessment ◽  
Reference Image

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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