scholarly journals Color Image Quality Assessment Measure Using Multivariate Generalized Gaussian Distribution

2013 ◽  
Author(s):  
Mounir Omari ◽  
Abdelkaher Ait Abdelouahad ◽  
Mohammed El Hassouni ◽  
Hocine Cherifi
Keyword(s):  
Image Quality ◽  
Quality Assessment ◽  
Gaussian Distribution ◽  
Image Quality Assessment ◽  
Color Image ◽  
Generalized Gaussian Distribution ◽  
Assessment Measure ◽  
Multivariate Generalized Gaussian Distribution

Blind Image Quality Assessment Based on Natural Statistics of Double-Opponency

2018 ◽  
Vol 22 (5) ◽  
pp. 725-730 ◽  
Author(s):  
Edwin Sybingco ◽  
◽  
Elmer P. Dadios
Keyword(s):  
Neural Network ◽  
Image Quality ◽  
Quality Assessment ◽  
Gaussian Distribution ◽  
Image Quality Assessment ◽  
Assessment Method ◽  
Feedforward Neural Network ◽  
Reference Image ◽  
Generalized Gaussian Distribution ◽  
Natural Statistics

One of the challenges in image quality assessment (IQA) is to determine the quality score without the presence of the reference image. In this paper, the authors proposed a no-reference image quality assessment method based on the natural statistics of double-opponent (DO) cells. It utilizes the statistical modeling of the three opponency channels using the generalized Gaussian distribution (GGD) and asymmetric generalized Gaussian distribution (AGGD). The parameters of GGD and AGGD are then applied to feedforward neural network to predict the image quality. Result shows that for any opposing channels, its natural statistics parameters when applied to feedforward neural network can achieve satisfactory prediction of image quality.

scholarly journals No-Reference Image Quality Assessment Based on Dual-Domain Feature Fusion

Entropy ◽  
2020 ◽  
Vol 22 (3) ◽  
pp. 344
Author(s):  
Yueli Cui
Keyword(s):  
Image Quality ◽  
Quality Assessment ◽  
Gaussian Distribution ◽  
Image Quality Assessment ◽  
Feature Fusion ◽  
Assessment Model ◽  
Reference Image ◽  
Generalized Gaussian Distribution ◽  
Dual Domain ◽  
Fitting Parameters

Image quality assessment (IQA) aims to devise computational models to evaluate image quality in a perceptually consistent manner. In this paper, a novel no-reference image quality assessment model based on dual-domain feature fusion is proposed, dubbed as DFF-IQA. Firstly, in the spatial domain, several features about weighted local binary pattern, naturalness and spatial entropy are extracted, where the naturalness features are represented by fitting parameters of the generalized Gaussian distribution. Secondly, in the frequency domain, the features of spectral entropy, oriented energy distribution, and fitting parameters of asymmetrical generalized Gaussian distribution are extracted. Thirdly, the features extracted in the dual-domain are fused to form the quality-aware feature vector. Finally, quality regression process by random forest is conducted to build the relationship between image features and quality score, yielding a measure of image quality. The resulting algorithm is tested on the LIVE database and compared with competing IQA models. Experimental results on the LIVE database indicate that the proposed DFF-IQA method is more consistent with the human visual system than other competing IQA methods.

scholarly journals A statistical reduced-reference method for color image quality assessment

2014 ◽  
Vol 74 (19) ◽  
pp. 8685-8701 ◽  
Author(s):  
Mounir Omari ◽  
Mohammed El Hassouni ◽  
Abdelkaher Ait Abdelouahad ◽  
Hocine Cherifi
Keyword(s):  
Image Quality ◽  
Quality Assessment ◽  
Image Quality Assessment ◽  
Color Image ◽  
Reference Method

scholarly journals A New No-reference Method for Color Image Quality Assessment

2012 ◽  
Vol 40 (17) ◽  
pp. 24-31 ◽  
Author(s):  
Sonia Ouni ◽  
Ezzeddine Zagrouba ◽  
Majed Chambah
Keyword(s):  
Image Quality ◽  
Quality Assessment ◽  
Image Quality Assessment ◽  
Color Image ◽  
Reference Method
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