Quality assessment of images with multiple distortions based on phase congruency and gradient magnitude

2019 ◽  
Vol 79 ◽  
pp. 54-62
Author(s):  
Xikui Miao ◽  
Hairong Chu ◽  
Hui Liu ◽  
Yao Yang ◽  
Xiaolong Li
Keyword(s):  
Quality Assessment ◽  
Phase Congruency ◽  
Gradient Magnitude

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.

Multiply-Distorted Image Quality Assessment Based on High-Order Phase Congruency

2017 ◽  
Vol 54 (7) ◽  
pp. 071001
Author(s):  
侯春萍 Hou Chunping ◽  
马彤彤 Ma Tongtong ◽  
岳广辉 Yue Guanghui ◽  
冯丹丹 Feng Dandan ◽  
刘 月 Liu Yue
Keyword(s):  
Image Quality ◽  
Quality Assessment ◽  
Image Quality Assessment ◽  
High Order ◽  
Phase Congruency ◽  
Distorted Image

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.

scholarly journals A Simple Quality Assessment Index for Stereoscopic Images Based on 3D Gradient Magnitude

2014 ◽  
Vol 2014 ◽  
pp. 1-11 ◽  
Author(s):  
Shanshan Wang ◽  
Feng Shao ◽  
Fucui Li ◽  
Mei Yu ◽  
Gangyi Jiang
Keyword(s):  
Quality Assessment ◽  
Image Quality Assessment ◽  
Subjective Assessment ◽  
Quality Score ◽  
3D Image ◽  
Stereoscopic Images ◽  
Assessment Index ◽  
Gradient Magnitude ◽  
High Consistency ◽  
3D Volume

We present a simple quality assessment index for stereoscopic images based on 3D gradient magnitude. To be more specific, we construct 3D volume from the stereoscopic images across different disparity spaces and calculate pointwise 3D gradient magnitude similarity (3D-GMS) along three horizontal, vertical, and viewpoint directions. Then, the quality score is obtained by averaging the 3D-GMS scores of all points in the 3D volume. Experimental results on four publicly available 3D image quality assessment databases demonstrate that, in comparison with the most related existing methods, the devised algorithm achieves high consistency alignment with subjective assessment.

Sign in / Sign up

Export Citation Format

Share Document