Hybrid No-Reference Natural Image Quality Assessment of Noisy, Blurry, JPEG2000, and JPEG Images

2011 ◽  
Vol 20 (8) ◽  
pp. 2089-2098 ◽  
Author(s):  
Ji Shen ◽  
Qin Li ◽  
G. Erlebacher
Keyword(s):  
Image Quality ◽  
Quality Assessment ◽  
Image Quality Assessment ◽  
Natural Image ◽  
Jpeg Images

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.

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.

Sign in / Sign up

Export Citation Format

Share Document