Fusing the pertinence of natural scene statistics-based methods and the ubiquity of convolutional neural network-based methods, a no-reference image quality assessment (IQA) method fusing deep learning and statistical visual features for no-reference image quality assessment (FDSVIQA) is proposed. For the statistical visual features, a local normalized luminance map and a local normalized local binary pattern (LBP) map of the image are constructed, and the local normalized luminance features and the gradient-weighted local normalized LBP features are extracted on the two maps, respectively. These two kinds of features are concatenated to build the image statistical visual features. For deep learning, the local normalized luminance block and the localized normalized LBP block are input into a double-path deep learning network, and the statistical visual features are input into the deep learning network to be integrated with the depth features. After learning and training, IQA is achieved. The performance of the proposed FDSVIQA algorithm is tested on the Laboratory for Image and Video Engineering (LIVE), LIVE Multiply Distorted Image Quality Database, and Multiply Distortion Optics Remote Sensing Image databases. Experimental results show that the FDSVIQA algorithm has excellent subjective and objective consistency and good robustness for both distorted natural images and distorted remote sensing images. In addition, the FDSVIQA has database independence.
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.
Scattering phase function on horizontally oriented ice particles near the specular reflective direction is analytically modeled using a mixed method combining direct reflection and Fraunhofer diffraction components, where particles are simply treated as circular facets and the effect of fluttering is introduced under the assumption of Gauss distribution. The obtained model expression reveals that the essence of far-field scattering around specular direction is the diffraction pattern modulated by fluttered geometric reflection. Four groups of experiments are designed to validate this model at different wavelengths and incidence angles, and the calculated phase functions present good agreement both in distributions and peak values with that of T-matrix method in conjunction with a Monte Carlo stochastic process.