Abstract
Considering the relatively poor real-time performance when extracting transform-domain image features and the insufficiency of spatial domain features extraction, a no-reference remote sensing image quality assessment method based on gradient-weighted spatial natural scene statistics is proposed. A 36-dimensional image feature vector is constructed by extracting the local normalized luminance features and the gradient-weighted local binary pattern features of local normalized luminance map in three scales. First, a support vector machine classifier is obtained by learning the relationship between image features and distortion types. Then based on the support vector machine classifier, the support vector regression scorer is obtained by learning the relationship between image features and image quality scores. A series of comparative experiments were carried out in the optics remote sensing image database, the LIVE database, the LIVEMD database, and the TID2013 database, respectively. Experimental results show the high accuracy of distinguishing distortion types, the high consistency with subjective scores, and the high robustness of the method for remote sensing images. In addition, experiments also show the independence for the database and the relatively high operation efficiency of this method.
Highlights
Optical remote sensing imaging is widely applied in many aspects such as weather forecast, environmental monitoring, resource detection, and military investigation
Considering the relatively poor real-time performance when extracting transform-domain image features and the insufficiency of spatial domain features extraction, a noreference remote sensing image quality assessment method based on gradient-weighted spatial natural scene statistics (GWNSS) is proposed in this paper
To illustrate the subjective consistency of the proposed GWNSS method, experiments of the proposed GWNSS and other existing IQA methods are performed on the optics remote sensing image database (ORSID) database,[10] the LIVE database,[18,19] and the LIVEMD database,[20] respectively
Summary
Optical remote sensing imaging is widely applied in many aspects such as weather forecast, environmental monitoring, resource detection, and military investigation. Block effect tend to generate in the process of compression transmission These factors degrade the remote sensing images and negatively affect their practical applications. Moorthy and Bovik[1] proposed a blind image quality index (BIQI), which extracts NSS features using two-step framework. Saad et al.[2] proposed blind image integrite notator using DCT statistics (BLIINDS-II), which extracts NSS features in discrete cosine transform (DCT) domain and calculate the quality score based on Bayesian model. Considering the relatively poor real-time performance when extracting transform-domain image features and the insufficiency of spatial domain features extraction, a noreference remote sensing image quality assessment method based on gradient-weighted spatial natural scene statistics (GWNSS) is proposed in this paper. A two-step framework based on SVM is used to obtain the relationship between features and distortion types as well as quality scores
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