Natural Image Quality Assessment Based on Visual Biological Cognitive Mechanism

2019 ◽  
Vol 7 (1) ◽  
pp. 1-26
Author(s):  
Run Zhang ◽  
Yongbin Wang
Keyword(s):  
Image Quality ◽  
Quality Assessment ◽  
Image Quality Assessment ◽  
Mixture Distribution ◽  
Gaussian Mixture ◽  
Theoretical Research ◽  
Distribution Model ◽  
Natural Image ◽  
Cognitive Computing ◽  
Cognitive Mechanism

With the focus of the main problems in no-reference natural image quality assessment (NR-IQA), the researchers propose a more universal, efficient and integrated resolution based on visual biological cognitive mechanism. First, the authors bring up an inspiring visual cognitive computing model (IVCCM) on the basis of visual heuristic principles. Second, the authors put forward an asymmetric generalized gaussian mixture distribution model (AGGMD), and the model can describe the probability distribution density of the images more precisely. Third, the authors extract the quality-aware multiscale local invariant features (QAMLIF) statistic and perceptive from natural images and form quality-aware uniform features descriptors (QAUFD) based on clustering and encoding the visual quality features. Fourth, the authors build topic semantic model and realize the resolution with Bayesian inference with IVCCM, AGGDM and QAUFD to implement NR-IQA. Theoretical research and experimental results show that the proposed resolution perform better with biological cognitive mechanism.

scholarly journals Blind Image Quality Assessment Based on Classification Guidance and Feature Aggregation

2020 ◽  
Author(s):  
WeiPeng Cai ◽  
Cien Fan ◽  
Lian Zou ◽  
Yifeng Liu ◽  
Yang Ma ◽  
...  
Keyword(s):  
Image Quality ◽  
Quality Assessment ◽  
Image Quality Assessment ◽  
Gaussian Mixture Models ◽  
Gaussian Mixture ◽  
Superior Performance ◽  
Blind Image Quality Assessment ◽  
Feature Aggregation ◽  
Hierarchical Features ◽  
Blind Image

Abstract In this work, we present a convolutional neural network (CNN) named CGFA-CNN for blind image quality assessment (BIQA). A unique 2-stage strategy is utilized which fifirstly identififies the distortion type in an image using Sub-network I and then quantififies this distortion using Sub-network II. And difffferent from most deep neural networks, we extract hierarchical features as descriptors to enhance the image representation and design a feature aggregation layer in an end-to-end training manner applying Fisher encoding to visual vocabularies modeled by Gaussian mixture models (GMMs). Considering the authentic distortions and synthetic distortions, the hierarchical feature contains the characteristics of a CNN trained on the self-built dataset and a CNN trained on ImageNet. We evaluate our algorithm on the four publicly available databases, and results demonstrate that our CGFA-CNN has superior performance over other methods both on synthetic and authentic databases.

Entirely Blind Image Quality Assessment Estimator

2014 ◽  
Vol 543-547 ◽  
pp. 2496-2499
Author(s):  
Abdalmajeed Saifeldeen ◽  
Shu Hong Jiao ◽  
Wei Liu
Keyword(s):  
Image Quality ◽  
Quality Assessment ◽  
Prior Knowledge ◽  
Image Quality Assessment ◽  
Signal To Noise Ratio ◽  
Structural Similarity ◽  
General Purpose ◽  
Reference Image ◽  
Natural Image ◽  
Natural Scenes

Prior knowledge about anticipated distortions and their corresponding human opinion scores is needed in the most general purpose no-reference image quality assessment algorithms. When creating the model, all distortion types may not be existed. Predicting the quality of distorted images in practical no-reference image quality assessment algorithms is devised without prior knowledge about images or their distortions. In this study, a blind/no-reference opinion and distortion unaware image quality assessment algorithm based on natural scenes is developed. The proposed approach uses a set of novel features to measure image quality in a spatial domain. The extracted features which are from the scenes gist are formed using Weibull distribution statistics. When testing the proposed algorithm on LIVE database, experiments show that it correlates well with subjective opinion scores. They also show that the proposed algorithm significantly outperforms the popular full-reference peak signal-to-noise ratio (PSNR) and structural similarity (SSIM) methods. Not only do the results reasonably well compete with the recently developed natural image quality evaluator (NIQE) model, but also outperform it.

scholarly journals Blind Image Quality Assessment Based on Classification Guidance and Feature Aggregation

Electronics ◽  
2020 ◽  
Vol 9 (11) ◽  
pp. 1811
Author(s):  
Weipeng Cai ◽  
Cien Fan ◽  
Lian Zou ◽  
Yifeng Liu ◽  
Yang Ma ◽  
...  
Keyword(s):  
Image Quality ◽  
Quality Assessment ◽  
Image Quality Assessment ◽  
Gaussian Mixture Models ◽  
Gaussian Mixture ◽  
Superior Performance ◽  
Blind Image Quality Assessment ◽  
Feature Aggregation ◽  
Hierarchical Features ◽  
Blind Image

In this work, we present a convolutional neural network (CNN) named CGFA-CNN for blind image quality assessment (BIQA). A unique two-stage strategy is utilized which firstly identifies the distortion type in an image using Sub-Network I and then quantifies this distortion using Sub-Network II. Different from most deep neural networks, we extract hierarchical features as descriptors to enhance the image representation and design a feature aggregation layer in an end-to-end training manner applying Fisher encoding to visual vocabularies modeled by Gaussian mixture models (GMMs). Considering the authentic distortions and synthetic distortions, the hierarchical feature contains the characteristics of a CNN trained on the self-built dataset and a CNN trained on ImageNet. We evaluated our algorithm on four publicly available databases, and the results demonstrate that our CGFA-CNN has superior performance over other methods both on synthetic and authentic databases.

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