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    Local structure divergence index for image quality assessment

    Gao, F. and Tao, D. and Li, Xuelong and Gao, X. and He, L. (2012) Local structure divergence index for image quality assessment. In: Huang, T. and Zeng, Z. and Li, C. and Leung, C.S. (eds.) Neural Information Processing. Berlin, Germany: Springer Verlag, pp. 337-344. ISBN 9783642345005.

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    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.


    Item Type: Book Section
    School: Birkbeck Faculties and Schools > Faculty of Science > School of Computing and Mathematical Sciences
    Depositing User: Sarah Hall
    Date Deposited: 06 Jun 2013 15:59
    Last Modified: 09 Aug 2023 12:33


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