Abstract
In this paper, we propose a novel image quality assessment (IQA) based on an Improved Structural SIMilarity (ISSIM) which considers the spatial distributions of image structures. The existing structural similarity (SSIM) metric, which measures structure loss based on statistical moments, i.e., the mean and variance, represents mainly the luminance change of pixels rather than describing the spatial distribution. However, the human visual system (HVS) is highly adapted to extract structures with regular spatial distributions. In this paper, we employ a self-similarity based procedure to describe the spatial distribution of image structures. Then, combining with the statistical characters, we improve the structural similarity based quality metric. Furthermore, considering the viewing condition, we extend the ISSIM metric to the multi-scale space. Experimental results demonstrate the proposed IQA metric is more consistent with the human perception than the SSIM metric.
This work is supported by Natural Science Foundation of China under Grant NO. 60805012, 61033004, 61070138, 61227004, and 61003148.
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References
Chen, G., Yang, C., Po, L., Xie, S.: Edge-Based structural similarity for image quality assessment. In: Proceedings of the 2006 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2006, vol. 2, p. II (May 2006)
Katkovnik, V., Foi, A., Egiazarian, K., Astola, J.: From local kernel to nonlocal Multiple-Model image denoising. Int. J. Computer Vision 86, 1–32 (2009)
Li, C., Bovik, A.C.: Three-component weighted structural similarity index, pp. 72420Q-1–72420Q–9. SPIE (2009)
Lin, W., Kuo, C.J.: Perceptual visual quality metrics: A survey. J. Visual Communication and Image Representation 22(4), 297–312 (2011)
Liu, A., Lin, W., Narwaria, M.: Image quality assessment based on gradient similarity. IEEE Transactions on Image Processing: A Publication of the IEEE Signal Processing Society 21(4), 1500–1512 (2012)
Ponomarenko, N., Lukin, V., Zelensky, A., Egiazarian, K., Carli, M., Battisti, F.: Tid2008 - a database for evaluation of full-reference visual quality assessment metrics. Advances of Modern Radioelectronics 10, 30–45 (2009)
Video Quality Expert Group (VQEG): Final report from the video quality experts group on the validation of objective models of video quality assessment ii (2003), http://www.vqeg.org/
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., Li, Q.: Information content weighting for perceptual image quality assessment. IEEE Transactions on Image Processing 20(5), 1185–1198 (2011)
Wang, Z., Simoncelli, E., Bovik, A.: Multiscale structural similarity for image quality assessment. In: Conference Record of the Thirty-Seventh Asilomar Conference on Signals, Systems and Computers, vol. 2, pp. 1398–1402 (2003)
Wu, J., Qi, F., Shi, G.: Self-similarity based structural regularity for just noticeable difference estimation. Journal of Visual Communication and Image Representation 23(6), 845–852 (2012)
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Wu, J., Qi, F., Shi, G. (2012). Image Quality Assessment Based on Improved Structural SIMilarity. In: Lin, W., et al. Advances in Multimedia Information Processing – PCM 2012. PCM 2012. Lecture Notes in Computer Science, vol 7674. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-34778-8_14
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DOI: https://doi.org/10.1007/978-3-642-34778-8_14
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