scholarly journals No-Reference Image Quality Assessment Based on the Fusion of Statistical and Perceptual Features

2020 ◽  
Vol 6 (8) ◽  
pp. 75
Author(s):  
Domonkos Varga
Keyword(s):  
Image Quality ◽  
Quality Assessment ◽  
Image Quality Assessment ◽  
Reference Image ◽  
Phase Congruency ◽  
Perceptual Features ◽  
Proposed Model ◽  
Dark Channel ◽  
Reference Images ◽  
Spatial Domains

The goal of no-reference image quality assessment (NR-IQA) is to predict the quality of an image as perceived by human observers without using any pristine, reference images. In this study, an NR-IQA algorithm is proposed which is driven by a novel feature vector containing statistical and perceptual features. Different from other methods, normalized local fractal dimension distribution and normalized first digit distributions in the wavelet and spatial domains are incorporated into the statistical features. Moreover, powerful perceptual features, such as colorfulness, dark channel feature, entropy, and mean of phase congruency image, are also incorporated to the proposed model. Experimental results on five large publicly available databases (KADID-10k, ESPL-LIVE HDR, CSIQ, TID2013, and TID2008) show that the proposed method is able to outperform other state-of-the-art methods.

A New Image Quality Assessment Method Based on SSIM and TV Model

2011 ◽  
Vol 65 ◽  
pp. 542-550
Author(s):  
Lu Lu Pang ◽  
Cong Li Li ◽  
De Ning Qi ◽  
Tao Zou
Keyword(s):  
Image Quality ◽  
Quality Assessment ◽  
Explicit Knowledge ◽  
Image Quality Assessment ◽  
Structural Similarity ◽  
Assessment Method ◽  
Reference Image ◽  
Distorted Image ◽  
Quality Assessment Method ◽  
Reference Images

In this paper, a new image quality assessment method has been proposed in which can judge the quality of images without explicit knowledge of the reference images ,it is based on the SSIM(Structural Similarity) and TV(total variation) model. Firstly, add noises to distorted image to quantitatively determine, it can get the degraded image; secondly, use the improved self-adaptive gradient weights of the TV algorithms to denoising the distorted image, it can get the “fake” reference image, then use the classical SSIM methods to make reference evaluation between the distorted image and the “fake” reference image, after modified, the results is the no reference evaluating indicator. The experiment separated use the standard testing images and the degraded images from the LIVE database to make evaluate experiment, the result show that it is consistent to the result of MOS. This method is no need of reference images, it can use widely.

scholarly journals A Full-Reference Image Quality Assessment for Multiply Distorted Image based on Visual Mutual Information

2019 ◽  
Vol 63 (6) ◽  
pp. 60504-1-60504-11
Author(s):  
Yin Zhang ◽  
Xuehan Bai ◽  
Junhua Yan ◽  
Yongqi Xiao ◽  
Wanyi Zhang ◽  
...  
Keyword(s):  
Image Quality ◽  
Mutual Information ◽  
Quality Assessment ◽  
Image Quality Assessment ◽  
Reference Image ◽  
Assessment Index ◽  
Distorted Image ◽  
Gaussian Scale Mixture ◽  
Full Reference ◽  
Reference Images

Abstract A full-reference image quality assessment (FR-IQA) method for multi-distortion based on visual mutual information (MD-IQA) is proposed to solve the problem that the existing FR-IQA methods are mostly applicable to single-distorted images, but the assessment result for multiply distorted images is not ideal. First, the reference image and the distorted image are preprocessed by steerable pyramid decomposition and contrast sensitivity function (CSF). Next, a Gaussian scale mixture (GSM) model and an image distorted model are respectively constructed for the reference images and the distorted images. Then, visual distorted models are constructed both for the reference images and the distorted images. Finally, the mutual information between the processed reference image and the distorted image is calculated to obtain the full-reference quality assessment index for multiply distorted images. The experimental results show that the proposed method has higher accuracy and better performance for multiply distorted images.

scholarly journals A technical review of no-reference image quality assessment algorithms for contrast distorted images

2021 ◽  
Author(s):  
Preeti Mittal ◽  
◽  
Rajesh Kumar Saini ◽  
Justin Varghese ◽  
Neeraj Kumar Jain ◽  
...  
Keyword(s):  
Image Quality ◽  
Quality Assessment ◽  
Real Time ◽  
Image Quality Assessment ◽  
Human Vision ◽  
Reference Image ◽  
Benchmark Assessment ◽  
Reference Images ◽  
Insight Into

Automatic image quality assessment similar to human vision perception is an essential process for real-time image processing applications to perform perceptual image assessments for effectively achieving their goals. As no-reference image quality assessment (NR-IQA) schemes perform perceptual assessments of images without any information about their original version, these algorithms suit real-time computer vision techniques because of the non-availability of reference images. Contrast and colorfulness play important roles in determining the quality of color images. By combining many IQA metrics, a number of combined metrics had been devised. This study provides an insight into major NR-IQA methods and their effectiveness in assessing contrast, colorfulness, and overall quality of contrast-degraded images with technical analysis. The effectiveness of top-ranking NR-IQA methods is experimentally assessed with benchmark assessment methods on images from benchmarked databases. The study provides insight into open research challenges in the area of NR-IQA for developing new promising methods by clearly demarcating the difficulties of top-ranking NR-IQA methods.

No-Reference Image Quality Assessment Based on Multi-Order Gradients Statistics

2020 ◽  
Vol 64 (1) ◽  
pp. 10505-1-10505-16
Author(s):  
Yin Zhang ◽  
Xuehan Bai ◽  
Junhua Yan ◽  
Yongqi Xiao ◽  
C. R. Chatwin ◽  
...  
Keyword(s):  
Neural Network ◽  
Image Quality ◽  
Quality Assessment ◽  
Image Quality Assessment ◽  
Assessment Method ◽  
Image Distortion ◽  
Image Feature ◽  
Reference Image ◽  
Gradient Magnitude ◽  
Distortion Type

Abstract A new blind image quality assessment method called No-Reference Image Quality Assessment Based on Multi-Order Gradients Statistics is proposed, which is aimed at solving the problem that the existing no-reference image quality assessment methods cannot determine the type of image distortion and that the quality evaluation has poor robustness for different types of distortion. In this article, an 18-dimensional image feature vector is constructed from gradient magnitude features, relative gradient orientation features, and relative gradient magnitude features over two scales and three orders on the basis of the relationship between multi-order gradient statistics and the type and degree of image distortion. The feature matrix and distortion types of known distorted images are used to train an AdaBoost_BP neural network to determine the image distortion type; the feature matrix and subjective scores of known distorted images are used to train an AdaBoost_BP neural network to determine the image distortion degree. A series of comparative experiments were carried out using Laboratory of Image and Video Engineering (LIVE), LIVE Multiply Distorted Image Quality, Tampere Image, and Optics Remote Sensing Image databases. Experimental results show that the proposed method has high distortion type judgment accuracy and that the quality score shows good subjective consistency and robustness for all types of distortion. The performance of the proposed method is not constricted to a particular database, and the proposed method has high operational efficiency.

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