No-reference objective blur metric based on the notion of wavelet gradient, magnitude edge width

2014 ◽  
Author(s):  
Soundararajan Ezekiel ◽  
Kyle Harrity ◽  
Mark Alford ◽  
Erik Blasch ◽  
David Ferris ◽  
...  
Keyword(s):  
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.

Road Detection Based on Edge Feature with GAC Model in Aerial Image

2021 ◽  
Author(s):  
Linying Zhou ◽  
Zhou Zhou ◽  
Hang Ning
Keyword(s):  
Transformation Method ◽  
Aerial Images ◽  
Aerial Image ◽  
Initial Contour ◽  
Detection Accuracy ◽  
Road Detection ◽  
Canny Operator ◽  
The Road ◽  
Gradient Magnitude ◽  
Edge Feature

Road detection from aerial images still is a challenging task since it is heavily influenced by spectral reflectance, shadows and occlusions. In order to increase the road detection accuracy, a proposed method for road detection by GAC model with edge feature extraction and segmentation is studied in this paper. First, edge feature can be extracted using the proposed gradient magnitude with Canny operator. Then, a reconstructed gradient map is applied in watershed transformation method, which is segmented for the next initial contour. Last, with the combination of edge feature and initial contour, the boundary stopping function is applied in the GAC model. The road boundary result can be accomplished finally. Experimental results show, by comparing with other methods in [Formula: see text]-measure system, that the proposed method can achieve satisfying results.

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

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.

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