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.

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.

A No-Reference Image Quality Comprehensive Assessment Method

2020 ◽  
pp. 2154011
Author(s):  
Yuan-Yuan Fan ◽  
Ying-Jun Sang
Keyword(s):  
Image Quality ◽  
Quality Assessment ◽  
Correlation Coefficient ◽  
Image Quality Assessment ◽  
Rank Order ◽  
Comprehensive Assessment ◽  
Assessment Model ◽  
Reference Image ◽  
Human Visual Perception ◽  
Function Model

On the basis of the research status of image quality comprehensive assessment, a no-reference image quality comprehensive assessment function model is proposed in this paper. First, the image quality is classified as contrast, sharpness, and signal-to-noise ratio (SNR), and the interrelation of each assessment index is researched and analyzed; second, the weights in the comprehensive assessment model are studied when only contrast, sharpness, and SNR are changed. Finally, on the basis of studying each kind of distortion separately, and considering the different types of image distortion, we studied how to determine the weights of each index in the comprehensive image quality assessment. The results show that the no-reference image quality comprehensive assessment function model proposed in this paper can better fit human visual perception, and it has a good correlation with Difference Mean Opinion Score (DMOS). Correlation Coefficient (CC) reached 0.8331, Spearman Rank Order Correlation Coefficient (SROCC) reached 0.8206, Mean Absolute Error (MAE) was only 0.0920, Root Mean Square Error (RMSE) was only 0.1122, Outlier Ratio (OR) was only 0.0365. The method proposed in this paper can be applied to photoelectric measurement equipment television system and give an accurate and reliable quality assessment to no reference television images.

An Image Quality Assessment Approach Based on Saliency Map in Space Domain

2014 ◽  
Vol 1006-1007 ◽  
pp. 768-772
Author(s):  
Bin Wang
Keyword(s):  
Image Quality ◽  
Gaussian Distribution ◽  
Image Quality Assessment ◽  
Saliency Map ◽  
Reference Image ◽  
Support Vector ◽  
Space Domain ◽  
Generalized Gaussian Distribution ◽  
Assessment Approach ◽  
Recent Method

This paper proposes a no-reference image assessment approach (IQA) based on saliency map in the space domain of the image. The saliency map of the image is extracted by Itti model at first. Next, the saliency-map weighted normalized image is used to get the histogram of the image, then the histogram is modeled by generalized gaussian distribution and the parameter of the generalized gaussian distribution is estimated by parameter estimating approach. Parameters of the generalized gaussian distribution are used as the feature vector for the training and testing image. The feature vectors of the testing image are fed to support vector regresion machine to evaluate the image quality score. Experimental results show that our approach outperforms the recent method of no-reference IQA.

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