Video Quality Assessment Using the Combined Full-Reference Approach

  • Krzysztof Okarma
Part of the Advances in Intelligent and Soft Computing book series (AINSC, volume 84)


In this paper the new combined video quality metric is proposed, which may be useful for the quality assessment of the compressed video files, especially transmitted using wireless channels. The proposed metric is the weighted combination of three state-of-the-art image quality metrics, which are well correlated with the subjective evaluations. A simple extension of those metrics for the video quality assessment is the averaging of their values for all video frames. Nevertheless, such approach may not lead to satisfactory results for all types of distortions. In this paper the typical distortions introduced during the wireless video transmission have been analyzed using the 160 files available as the LIVE Wireless Video Quality Assessment Database together with the results of subjective quality evaluation. Obtained results are promising and the proposed metric is superior to each of the analyzed ones in the aspect of the linear correlation with subjective scores.


Singular Value Decomposition Video Quality Mean Opinion Score Image Quality Assessment Pearson Linear Correlation 
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  1. 1.
    Toet, A., Lucassen, M.P.: A new universal colour image fidelity metric. Displays 24(4-5), 197–207 (2003)CrossRefGoogle Scholar
  2. 2.
    Okarma, K.: Colour image quality assessment using Structural Similarity index and Singular Value Decomposition. In: Bolc, L., Kulikowski, J.L., Wojciechowski, K. (eds.) ICCVG 2008. LNCS, vol. 5337, pp. 55–65. Springer, Heidelberg (2009)Google Scholar
  3. 3.
    Wang, Z., Simoncelli, E.: Reduced-reference image quality assessment using a wavelet-domain natural image statistic model. In: Proc. Human Vision and Electronic Imaging Conf. Proceedings of SPIE, vol. 5666, pp. 149–159 (2005)Google Scholar
  4. 4.
    Li, X.: Blind image quality assessment. In: Proc. IEEE Int. Conf. Image Proc., pp. 449–452 (2002)Google Scholar
  5. 5.
    Marziliano, P., Dufaux, F., Winkler, S., Ebrahimi, T.: A no-reference perceptual blur metric. In: Proc. IEEE Int. Conf. Image Proc., pp. 57–60 (2002)Google Scholar
  6. 6.
    Ong, E.P.: Lin, Lu.W,, Yang, Z., Yao, S., Pan, F., Jiang, L., Moschetti, F.: A no-reference quality metric for measuring image blur. In: Proc. 7th Int. Symp. Signal Proc. and Its Applications, pp. 469–472 (2003)Google Scholar
  7. 7.
    Wang, Z., Sheikh, H., Bovik, A.: No-reference perceptual quality assessment of JPEG compressed images. In: Proc. IEEE Int. Conf. Image Proc., pp. 477–480 (2002)Google Scholar
  8. 8.
    Wang, Z., Bovik, A., Evans, B.: Blind measurement of blocking artifacts in images. In: Proc. IEEE Int. Conf. Image Proc., pp. 981–984 (2000)Google Scholar
  9. 9.
    Eskicioglu, A., Fisher, P., Chen, S.: Image quality measures and their performance. IEEE Trans. Comm. 43(12), 2959–2965 (1995)CrossRefGoogle Scholar
  10. 10.
    Eskicioglu, A.: Quality measurement for monochrome compressed images in the past 25 years. In: Proc. Int. Conf. Acoust. Speech Signal Proc., pp. 1907–1910 (2000)Google Scholar
  11. 11.
    Wang, Z., Bovik, A.: A universal image quality index. IEEE Signal Proc. Letters 9(3), 81–84 (2002)CrossRefGoogle Scholar
  12. 12.
    Wang, Z., Bovik, A., Sheikh, H., Simoncelli, E.: Image quality assessment: From error measurement to Structural Similarity. IEEE Trans. Image Proc. 13(4), 600–612 (2004)CrossRefGoogle Scholar
  13. 13.
    Okarma, K.: Two-dimensional windowing in the Structural Similarity index for the colour image quality assessment. In: Jiang, X., Petkov, N. (eds.) CAIP 2009. LNCS, vol. 5702, pp. 501–508. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  14. 14.
    Wang, Z., Simoncelli, E., Bovik, A.: Multi-Scale Structural Similarity for image quality assessment. In: Proc. 37th IEEE Asilomar Conf. on Signals, Systems and Computers (2003)Google Scholar
  15. 15.
    Shnayderman, A., Gusev, A., Eskicioglu, A.: A multidimensional image quality measure using Singular Value Decomposition. In: Proc. SPIE Image Quality and Syst. Perf., vol. 5294(1), pp. 82–92 (2003)Google Scholar
  16. 16.
    Shnayderman, A., Gusev, A., Eskicioglu, A.: An SVD-based gray-scale image quality measure for local and global assessment. IEEE Trans. Image Proc. 15(2), 422–429 (2006)CrossRefGoogle Scholar
  17. 17.
    Mahmoudi-Aznaveh, A., Mansouri, A., Torkamani-Azar, F., Eslami, M.: Image quality measurement besides distortion type classifying. Optical Review 16(1), 30–34 (2009)CrossRefGoogle Scholar
  18. 18.
    Mansouri, A., Mahmoudi-Aznaveh, A., Torkamani-Azar, F., Jahanshahi, J.A.: Image quality assessment using the Singular Value Decomposition theorem. Optical Review 16(2), 49–53 (2009)CrossRefGoogle Scholar
  19. 19.
    Sheikh, H.R., Bovik, A., de Veciana, G.: An information fidelity criterion for image quality assessment using natural scene statistics. IEEE Trans. Image Proc. 14(12), 2117–2128 (2005)CrossRefGoogle Scholar
  20. 20.
    Sheikh, H.R., Bovik, A.: Image information and visual quality. IEEE Trans. Image Proc. 15(2), 430–444 (2006)CrossRefGoogle Scholar
  21. 21.
    VQEG Final report on the validation of objective models of video quality assessment (2003),
  22. 22.
    Sendashonga, M., Labeau, F.: Low complexity image quality assessment using frequency domain transforms. In: Proc. IEEE Int. Conf. Image Proc., pp. 385–388 (2006)Google Scholar
  23. 23.
    Okarma, K.: Combined full-reference image quality metric linearly correlated with subjective assessment. In: Rutkowski, L., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds.) ICAISC 2010. LNCS (LNAI), vol. 6113, pp. 539–546. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  24. 24.
    Moorthy, A.K., Seshadrinathan, K., Soundararajan, R., Bovik, A.: Wireless video quality assessment: A study of subjective scores and objective algorithms. IEEE Trans. Circuits and Systems for Video Technology 20(4), 513–516 (2010)CrossRefGoogle Scholar
  25. 25.
    Moorthy, A.K., Seshadrinathan, K., Soundararajan, R., Bovik, A.: LIVE Wireless Video Quality Assessment Database (2009),
  26. 26.
    Okarma, K., Lech, P.: A statistical reduced-reference approach to digital image quality assessment. In: Bolc, L., Kulikowski, J.L., Wojciechowski, K. (eds.) ICCVG 2008. LNCS, vol. 5337, pp. 43–54. Springer, Heidelberg (2009)Google Scholar

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© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Krzysztof Okarma
    • 1
  1. 1.Faculty of Electrical Engineering, Department of Signal Processing and Multimedia EngineeringWest Pomeranian University of TechnologySzczecinPoland

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