scholarly journals Statistical Evaluation of No-Reference Image Quality Assessment Metrics for Remote Sensing Images

2017 ◽  
Vol 6 (5) ◽  
pp. 133 ◽  
Author(s):  
Shuang Li ◽  
Zewei Yang ◽  
Hongsheng Li
Keyword(s):  
Remote Sensing ◽  
Image Quality ◽  
Quality Assessment ◽  
Image Quality Assessment ◽  
Statistical Evaluation ◽  
Reference Image ◽  
Remote Sensing Images ◽  
Assessment Metrics

scholarly journals QUALITY ASSESSMENT OF PANSHARPENED RASAT AND LANDSAT-8 IMAGES USING SYNTHETIC SENTINEL-2 PANCHROMATIC IMAGE

2021 ◽  
Vol XLVI-4/W5-2021 ◽  
pp. 231-234
Author(s):  
M. A. Günen
Keyword(s):  
Image Quality ◽  
Quality Assessment ◽  
Spatial Resolution ◽  
Image Quality Assessment ◽  
Reference Image ◽  
Landsat 8 ◽  
Data Generation ◽  
Gis Applications ◽  
Assessment Metrics ◽  
Sentinel 2

Abstract. Technical and physical limitations often do not allow images to be acquired with high spatial and spectral resolution. Pansharpened images obtained by fusing high spatial resolution panchromatic images and multi-spectral images are widely used in GIS applications. In this study, it is aimed to increase the spatial resolution of the RASAT and Landsat-8 multispectral satellite images with synthetic Sentinel-2 panchromatic data. Six different pansharpening methods were used to test the success of the synthetic panchromatic data generation method using dataset with two different land use/land cover properties. Seven full reference image quality assessment metrics and two referenceless image quality assessment metrics were used to perform quantitative comparison.

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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