scholarly journals Three Different Features Based Metric To Assess Image Quality Blindly

2020 ◽  
Vol 8 (2) ◽  
pp. 97-103
Author(s):  
Saifeldeen Abdalmajeed Mahmood
Keyword(s):  
Image Quality ◽  
Gaussian Distribution ◽  
Image Quality Assessment ◽  
Signal To Noise Ratio ◽  
Structural Similarity ◽  
Distribution Model ◽  
Natural Scenes ◽  
Accuracy Evaluation ◽  
Opinion Score ◽  
Derivatives Of

Abstract When creating image quality assessment metric (IQA) no confirmation all distortion types are available. Non-specific distortion blind/no-reference (NR) IQA algorithms mostly need prior knowledge about anticipated distortions. This paper introduce a generic and distortion unaware (DU) approach for IQA with No Reference (NR). The approach uses three different measuring features which are initiated from the gist of natural scenes (NS) using Log-derivatives of the parameters; a general Gaussian distribution model, two sharpness functions, and Weibull distribution. All features were analyzed and co mpared together to examine their performance. When calibrating the proposed features performance on LIVE database, experiments show they have good contribution to the state of the art IQA and they outperform the popular full-reference peak signal-to-noise ratio (PSNR) and structural similarity (SSIM) methods. Also they show sharpness features are the best when assess both prediction monotonicity and predict accuracy evaluation among the three features categories. Besides they show asymmetric generalized Gaussian distribution (AGGD) based features have the best correlation with differential mean opinion score.

scholarly journals Using the Natural Scenes’ Edges for Assessing Image Quality Blindly and Efficiently

2015 ◽  
Vol 2015 ◽  
pp. 1-9 ◽  
Author(s):  
Saifeldeen Abdalmajeed ◽  
Jiao Shuhong
Keyword(s):  
Image Quality ◽  
Image Quality Assessment ◽  
Signal To Noise Ratio ◽  
Structural Similarity ◽  
Natural Image ◽  
Natural Scenes ◽  
Signal To Noise ◽  
Enhanced Sensitivity ◽  
Full Reference ◽  
Distortion Type

Two real blind/no-reference (NR) image quality assessment (IQA) algorithms in the spatial domain are developed. To measure image quality, the introduced approach uses an unprecedented concept for gathering a set of novel features based on edges of natural scenes. The enhanced sensitivity of the human eye to the information carried by edge and contour of an image supports this claim. The effectiveness of the proposed technique in quantifying image quality has been studied. The gathered features are formed using both Weibull distribution statistics and two sharpness functions to devise two separate NR IQA algorithms. The presented algorithms do not need training on databases of human judgments or even prior knowledge about expected distortions, so they are real NR IQA algorithms. In contrast to the most general no-reference IQA, the model used for this study is generic and has been created in such a way that it is not specified to any particular distortion type. When testing the proposed algorithms on LIVE database, experiments show that they correlate well with subjective opinion scores. They also show that the introduced methods significantly outperform the popular full-reference peak signal-to-noise ratio (PSNR) and the structural similarity (SSIM) methods. Besides they outperform the recently developed NR natural image quality evaluator (NIQE) model.

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 Full-Reference Facial Image Quality Assessment and Identification by Two Proposed Measures

2020 ◽  
Vol 55 (2) ◽  
Author(s):  
Rasha Ali Dihin ◽  
Nisreen Ryadh Hamza ◽  
Zinah Hussein Toman
Keyword(s):  
Image Quality ◽  
Similarity Measure ◽  
Image Quality Assessment ◽  
Mean Squared Error ◽  
Signal To Noise Ratio ◽  
Structural Similarity ◽  
Color Images ◽  
Facial Image ◽  
Histogram Similarity ◽  
Structural Similarity Measure

In this paper, the goal was to identify a person’s face in the acquired image by the proposed measures. We discuss the appearance of two types of noise together in an image. The acquired facial image quality was also assessed by two proposed measures, the histogram similarity measure and the histogram error mean measure. The histogram structural similarity measure is a previously described modified version of the information-theoretic structural similarity measure. It was merged with the structural similarity measure and the error mean measure, derived from the mean squared error, to get the proposed measures. The first proposed histogram similarity measure consists of merging histogram structural similarity with structural similarity measure, and the second proposed histogram error mean measure consists of merging histogram structural similarity with error mean measure. Finally, many algorithms for identification have recently been proposed to measure the similarity between two images. The results showed that the two proposed measures were better than existing methods. Different noises types (such as white Gaussian, speckle, and salt-and-pepper) are used with the proposed methods. Two facial image datasets were used in this paper. The AT&T database included color images of 92 x 112 pixels (px), and the Faculty of Industrial Engineering database included color images of 480 x 640 px. To evaluate performance and quantify the error, the structural similarity measure, histogram structural similarity, and error mean measure were considered. Noise ratios that depended on a peak signal-to-noise ratio were used in this experiment.

GPU Based Image Quality Assessment using Structural Similarity (SSIM) Index

2016 ◽  
pp. 276-282
Author(s):  
Mahesh Satish Khadtare
Keyword(s):  
Image Quality ◽  
Quality Assessment ◽  
Video Processing ◽  
Image Quality Assessment ◽  
Assessment Tool ◽  
Signal To Noise Ratio ◽  
Similarity Index ◽  
Structural Similarity ◽  
University Of Texas ◽  
Computationally Intensive

This chapter deals with performance analysis of CUDA implementation of an image quality assessment tool based on structural similarity index (SSI). Since it had been initial created at the University of Texas in 2002, the Structural SIMilarity (SSIM) image assessment algorithm has become a valuable tool for still image and video processing analysis. SSIM provided a big giant over MSE (Mean Square Error) and PSNR (Peak Signal to Noise Ratio) techniques because it way more closely aligned with the results that would have been obtained with subjective testing. For objective image analysis, this new technique represents as significant advancement over SSIM as the advancement that SSIM provided over PSNR. The method is computationally intensive and this poses issues in places wherever real time quality assessment is desired. We tend to develop a CUDA implementation of this technique that offers a speedup of approximately 30 X on Nvidia GTX275 and 80 X on C2050 over Intel single core processor.

Image Quality Assessment: From Error Visibility to Structural Similarity

2004 ◽  
Vol 13 (4) ◽  
pp. 600-612 ◽  
Author(s):  
Z. Wang ◽  
A.C. Bovik ◽  
H.R. Sheikh ◽  
E.P. Simoncelli
Keyword(s):  
Image Quality ◽  
Quality Assessment ◽  
Image Quality Assessment ◽  
Structural Similarity

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.

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