A New Image Quality Assessment Method Based on SSIM and TV Model

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In this paper, a new image quality assessment method has been proposed in which can judge the quality of images without explicit knowledge of the reference images ,it is based on the SSIM(Structural Similarity) and TV(total variation) model. Firstly, add noises to distorted image to quantitatively determine, it can get the degraded image; secondly, use the improved self-adaptive gradient weights of the TV algorithms to denoising the distorted image, it can get the “fake” reference image, then use the classical SSIM methods to make reference evaluation between the distorted image and the “fake” reference image, after modified, the results is the no reference evaluating indicator. The experiment separated use the standard testing images and the degraded images from the LIVE database to make evaluate experiment, the result show that it is consistent to the result of MOS. This method is no need of reference images, it can use widely.

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542-550

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June 2011

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© 2011 Trans Tech Publications Ltd. All Rights Reserved

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[1] Z. Wang, H.R. Sheikh, A.C. Bovik. Objective video quality assessment, The Handbook of Video Databases: Design and Applications[M ]. CRC Press, 2003. 1041-1078.

Google Scholar

[2] Winkler S. Vision Models and Quality Metrics for Image Processing Applications[D]. Germany: Diplom-lngenieur der Elektro technik, Technische Universitabt Wien de nationalitéautrich ienne, (2000).

Google Scholar

[3] Z. Wang A.C. Bovik H.R. Sheikh E.P. Simoncelli. Image quality assessment: from error visibility to structural similarity[J], IEEE Transactions on Image Processing 13(2004)600-612.

DOI: 10.1109/tip.2003.819861

Google Scholar

[4] P. Perona, J. Malik. Scale-space and edge detection using anisotropic diffusion, IEEE Trans. PAMI., vol. 12, no. 7, pp.720-727, (1990).

Google Scholar

[5] L. Rudin,S. Osher,E. Fatemi. Nonlinear total variation based noise removal algorithms, Phys. D: Nonlinear Phenom, vol. 60, pp.259-268, (1992).

DOI: 10.1016/0167-2789(92)90242-f

Google Scholar

[6] Tony F. Chan, Jianhong Shen. Image Processing and Analysis: Variational, PDE, Wavelet and Stochastic Methods. Beijing: Science Press, 2009, 01.

Google Scholar

[7] S D Rane, G. Sapiro, M. Bertalmio. Structure and texture filling in of missing image blocks in wireless transmission and compression applications, IEEE Trans. Image Process., vo1. 12, No. 3, pp.296-303, (2003).

DOI: 10.1109/tip.2002.804264

Google Scholar

[8] H.R. Sheikh,Z. Wang, CORMACKL, et al. LIVE image quality assessment database[DB/OL]. http: /live. ece. utexas. edu/research /quality, (2006).

Google Scholar

[9] Song B. Topics in Variational PDE Image Segmentation, Impainting and Denoising[D]. USA: UCLA, (2003).

Google Scholar

[10] Kendall M G. Multivariate analysis [M ]. London, U.K.: Griffin, (1975).

Google Scholar