Author:
Fu Hao,Liu Guojun,Yang Xiaoqin,Wei Lili,Yang Lixia
Abstract
No reference image quality assessment (NR IQA) aims to develop quantitative measures to automatically and accurately estimate perceptual image quality without any prior information about the reference image. In this paper, we introduce two low-level feature distributions (TLLFD) based method for NR IQA. Different from the deep learning method, the proposed method characterizes image quality with the distributions of low-level features, thus it has few parameters, simple model, high efficiency, and strong robustness. First, the texture change of distorted image is extracted by the weighted histogram of generalized local binary pattern. Second, the Weibull distribution of gradient is extracted to represent the structural change of the distorted image. Furthermore, support vector regression is adopted to model the complex nonlinear relationship between feature space and quality measure. Finally, numerical tests are performed on LIVE, CISQ, MICT, and TID2008 standard databases for five different distortion categories JPEG2000 (JP2K), JPEG, White Noise (WN), Gaussian Blur (GB), and Fast Fading (FF). The experimental results indicate that TLLFD method achieves superior performance and strong generalization for image quality prediction as compared to state-of-the-art full-reference, no reference, and even deep learning IQA methods.
Funder
National Natural Science Foundation of China
Key research and development programs of Ningxia
Natural Science Foundation of Ningxia
Subject
Fluid Flow and Transfer Processes,Computer Science Applications,Process Chemistry and Technology,General Engineering,Instrumentation,General Materials Science
Cited by
1 articles.
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