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.
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References
Tao, D.C., Li, X.L., Lu, W., Gao, X.B.: Reduced-reference iqa in contourlet do- main. IEEE Trans. Systems, Man, and Cybernetics, Part B 39(6), 1623–1627 (2009)
Gao, X.B., Lu, W., Tao, D.C., Li, X.L.: Image quality assessment based on multi- scale geometric analysis. IEEE Trans. Image Processing 18(7), 1409–1423 (2009)
Wang, Z., Bovik, A., Sheikh, H., Simoncelli, E.: Image quality assessment: From error visibility to structural similarity. IEEE Transactions on Image Processing 13(4), 600–612 (2004)
Wang, Z., Simoncelli, E., Bovik, A.: Multiscale structural similarity for image qual- ity assessment. In: Conference Record of the Thirty-Seventh Asilomar Conference on Signals, Systems and Computers, vol. 2, pp. 1398–1402 (2003)
Chen, G., Yang, C., Xie, S.: Gradient-based structural similarity for image quality assessment. In: 2006 IEEE International Conference on Image Processing, pp. 2929–2932 (2006)
Field, D.: Relations between the statistics of natural images and the response properties of cortical cells. Journal of the Optical Society of America 4(12), 2379–2394 (1987)
Takeda, H., Farsiu, S., Milanfar, P.: Kernel regression for image processing and reconstruction. IEEE Transactions on Image Processing 16(2), 349–366 (2007)
Tao, D.C., Li, X.L., Wu, X.D., Maybank, S.J.: Geometric mean for subspace selection. IEEE Trans. Pattern Anal. Mach. Intell. 31(2), 260–274 (2009)
Tao, D.C., Li, X.L., Wu, X.D., Maybank, S.J.: General tensor discriminant analysis and gabor features for gait recognition. IEEE Trans. Pattern Anal. Mach. Intell. 29(10), 1700–1715 (2007)
Sheikh, H., Bovik, A.: Image information and visual quality. IEEE Transactions on Image Processing 15(2), 430–444 (2006)
Zhang, L., Mou, X., Zhang, D.: Fsim: A feature similarity index for image quality assessment. IEEE Transactions on Image Processing 20(8), 2378–2386 (2011)
Sheikh, H., Wang, Z., Cormack, L., Bovik, A.: Live image quality assessment database release 2. Available (2005)
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© 2012 Springer-Verlag Berlin Heidelberg
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Gao, F., Tao, D., Li, X., Gao, X., He, L. (2012). Local Structure Divergence Index for Image Quality Assessment. In: Huang, T., Zeng, Z., Li, C., Leung, C.S. (eds) Neural Information Processing. ICONIP 2012. Lecture Notes in Computer Science, vol 7667. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-34500-5_40
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DOI: https://doi.org/10.1007/978-3-642-34500-5_40
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-34499-2
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