Local structure divergence index for image quality assessment

Authors:
Fei Gao;Dacheng Tao;Xuelong Li;Xinbo Gao;Lihuo He
Affiliations:
School of Electronic Engineering, Xidian University, Xi'an, Shaanxi, China;Centre for Quantum Computation and Intelligent Systems, Faculty of Engineering and Information Technology, University of Technology, Sydney, NSW, Australia;Center for OPTical IMagery Analysis and Learning (OPTIMAL), State Key Laboratory of Transient Optics and Photonics, Xi'an Institute of Optics and Precision Mechanics, Chinese Academy of Sciences, ...;School of Electronic Engineering, Xidian University, Xi'an, Shaanxi, China;School of Electronic Engineering, Xidian University, Xi'an, Shaanxi, China
Venue:
ICONIP'12 Proceedings of the 19th international conference on Neural Information Processing - Volume Part V
Year:
2012

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Abstract

Image quality assessment (IQA) algorithms are important for image-processing systems. And structure information plays a significant role in the development of IQA metrics. In contrast to existing structure driven IQA algorithms that measure the structure information using the normalized image or gradient amplitudes, we present a new Local Structure Divergence (LSD) index based on the local structures contained in an image. In particular, we exploit the steering kernels to describe local structures. Afterward, we estimate the quality of a given image by calculating the symmetric Kullback-Leibler divergence (SKLD) between kernels of the reference image and the distorted image. Experimental results on the LIVE database II show that LSD performs consistently with the human perception with a high confidence, and outperforms representative structure driven IQA metrics across various distortions.