Abstract
In this paper, we propose a new reduced reference image quality assessment (RR IQA) algorithm based on the image statistics. The image statistics is modeled in pixel domain, which is based on the gradient distribution of image. Compared with frequency domain coefficients, gradients are more easily calculated. The change of statistics in the gradient domain is measured to evaluate image distortion. To solve this problem, we fit the marginal distribution of image gradients to the integrated Weibull distribution locally. Then the estimated model parameters are extracted as the quality feature. We further propose a new RR IQA metric by quantifying the similarity between the original and the distorted quality features. Experimental results show that the proposed metric outperforms the well known RR IQA metric and has a comparable performance with the widely used full reference IQA metric Peak Signal to Noise Ratio (PSNR).
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References
Wang, Z., Bovik, A.: Modern image quality assessment. Morgan & Claypool (2006)
Gunawan, I.P., Ghanbari, M.: Reduced-reference picture quality estimation by using local harmonic amplitude information. In: Proc. London Communication Symposium, pp. 137–140 (September 2003)
Kusuma, T.M., Zepernick, H.J.: A reduced-reference perceptual quality metric for in-service image quality assessment. In: Proc. Joint First Workshop on Mobile Future and Symposium on Trends in Communications, pp. 71–74 (October 2003)
Elder, J., Zucker, S.W.: Evidence for boundary-specific grouping in human vision. Vision Research 38(1), 143–152 (1998)
Chen, G., Yang, C.L., Xie, S.: Gradient-based structural similarity for image quality assessment. In: IEEE International Conference on Image Processing, Atlanta, USA, pp. 2929–2932 (October 2006)
Li, Q., Wang, Z.: Reduced-reference image quality assessment using divisive normalization-based image representation. IEEE Journal of Selected Topics in Signal Processing 3(2), 202–211 (2009)
Yanulevskaya, V., Geusebroek, J.: Salient region detection from natural image statistics. In: Proc. the Fourteenth Annual Conference of the Advanced School for Computing and Imaging, Heijen, The Netherlands, pp. 389–395 (June 2008)
Wang, Z., Wu, G.X., Sheikh, H.R., Simoncelli, E.P., Yang, E.H., Bovik, A.C.: Quality-aware images. IEEE Transactions on Image Processing 15(6), 1680–1689 (2006)
Sheikh, H., Wang, Z., Cormack, L., Bovik, A.: Live image quality assessment database release 2, http://live.ece.utexas.edu/research/quality
VQEG, Final report from the video quality experts group on the validation of objective models of video quality assessment, phase II (2003), http://www.vqeg.org
van Gemert, J., Geusebroek, J., Veenman, C., Snoek, C., Smeulders, A.: Robust scene categorization by learning image statistics in context. In: Proc. CVPR Workshop on Semantic Learning Applications in Multimedia, p. 105 (June 2006)
Carnec, M., Le Callet, P., Barba, D.: An image quality assessment method based on perception of structural information. In: Proc. IEEE International Conference on Image Processing, Barcelona, Catalonia, Spain, pp. 185–188 (September 2003)
Carnec, M., Le Callet, P., Barba, D.: Visual features for image quality assessment with reduced reference. In: Proc. IEEE International Conference on Image Processing, Genoa, Italy, pp. 421–424 (September 2005)
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Chen, X., Zheng, S., Zhang, R. (2012). Reduced Reference Image Quality Assessment Based on Image Statistics in Pixel Domain. In: Zhang, W., Yang, X., Xu, Z., An, P., Liu, Q., Lu, Y. (eds) Advances on Digital Television and Wireless Multimedia Communications. Communications in Computer and Information Science, vol 331. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-34595-1_21
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DOI: https://doi.org/10.1007/978-3-642-34595-1_21
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