Mobile Video Quality Assessment: A Current Challenge for Combined Metrics

  • Krzysztof OkarmaEmail author
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 285)


Rapid development of mobile devices such as smartphones and tablets causes the growing interest in video transmission and display dedicated for mobile devices. Considering the typical distortions introduced mainly by video compression and transmission errors, their influence on the perceived video quality is not necessarily very similar to subjective evaluation of still images or videos presented using typical computers equipped with monitors. Therefore, there is a need of verification of usefulness of known image and video quality metrics for this purpose together with recently proposed combined metrics leading to highly linear correlation with subjective quality evaluations. In this paper some results of such verifications conducted using LIVE Mobile Video Quality Database as well as results of optimisation of proposed combined metric are presented. Obtained results are superior in comparison to other known metrics applied using frame-by-frame approach.


Video quality assessment Combined metrics Mobile video 


  1. 1.
    Aja-Fernandez, S., Estepar, R.S.J., Alberola-Lopez, C., Westiniu, C.F.: Image quality assessment based on local variance. In: 28th IEEE Annual International Conference on Engineering in Medicine and Biology Society (EMBS), pp 4815–4818. New York City (2006)Google Scholar
  2. 2.
    Chen, G.H., Yang, C.L., Xie, S.L.: Gradient-based structural similarity for image quality assessment. In: Proceedings of 13th IEEE International Conference on Image Processing (ICIP), pp. 2929–2932. Atlanta, Georgia (2006)Google Scholar
  3. 3.
    Liu, T.J., Lin, W., Kuo, C.C.J.: Image quality assessment using multi-method fusion. IEEE Trans. Image Process. 22(5), 1793–1807 (2013)CrossRefMathSciNetGoogle Scholar
  4. 4.
    Liu, Z., Laganière, R.: Phase congruence measurement for image similarity assessment. Pattern Recogn. Lett. 28(1), 166–172 (2007)CrossRefGoogle Scholar
  5. 5.
    Mansouri, A., Mahmoudi-Aznaveh, A., Torkamani-Azar, F., Jahanshahi, J.: Image quality assessment using the Singular Value Decomposition theorem. Opt. Rev. 16(2), 49–53 (2009)CrossRefGoogle Scholar
  6. 6.
    Moorthy, A.K., Choi, L.K., Bovik, A.C., de Veciana, G.: Video quality assessment on mobile devices: subjective, behavioral and objective studies. IEEE J. Sel. Top. Sign. Proces. 6(6), 652–671 (2012)CrossRefGoogle Scholar
  7. 7.
    Moorthy, A.K., Choi, L.K., de Veciana, G., Bovik, A.C.: Mobile Video Quality Assessment Database. In: IEEE ICC Workshop on Realizing Advanced Video Optimized Wireless Networks, pp. 7055–7059. Ottawa, Canada (2012)Google Scholar
  8. 8.
    Moorthy, A.K., Choi, L.K., de Veciana, G., Bovik, A.C.: Subjective analysis of video quality on mobile devices. In: 6th International Workshop on Video Processing and Quality Metrics for Consumer Electronics (VPQM), pp. 63–68. Scottsdale, Arizona (2012)Google Scholar
  9. 9.
    Okarma, K.: Colour image quality assessment using structural similarity index and Singular Value Decomposition. In: Bolc, L., Kulikowski, J., Wojciechowski, K. (eds.) ICCVG 2008. LNCS, vol. 5337, pp. 55–65. Springer, Heidelberg (2009)Google Scholar
  10. 10.
    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)Google Scholar
  11. 11.
    Okarma, K.: Combined full-reference image quality metric linearly correlated with subjective assessment. In: Rutkowski, L., Scherer, R., Tadeusiewicz, R., Zadeh, L., Zurada, J. (eds.) ICAISC 2010. LNCS, vol. 6113, pp. 539–546. Springer, Heidelberg (2010)Google Scholar
  12. 12.
    Okarma, K.: Video quality assessment using the combined full-reference approach. In: Choraś, R.S. (ed.) IP&C 2010. AISC, vol. 84, pp. 51–58. Springer, Heidelberg (2010)Google Scholar
  13. 13.
    Okarma, K.: Combined image similarity index. Opt. Rev. 19(5), 249–254 (2012)CrossRefGoogle Scholar
  14. 14.
    Okarma, K.: Weighted feature similarity—a nonlinear combination of gradient and phase congruency for full-reference image quality assessment. In: Choraś, R.S. (ed.) IP&C 2012. AISC, vol. 184, pp. 187–194. Springer, Heidelberg (2013)Google Scholar
  15. 15.
    Sheikh, H., Bovik, A.C.: Image information and visual quality. IEEE Trans. Image Process. 15(2), 430–444 (2006)CrossRefGoogle 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 Process. 15(2), 422–429 (2006)CrossRefGoogle Scholar
  17. 17.
    Wang, Z., Bovik, A.C.: A universal image quality index. IEEE Signal Process. Lett. 9(3), 81–84 (2002)CrossRefGoogle Scholar
  18. 18.
    Wang, Z., Bovik, A.C., Sheikh, H., Simoncelli, E.: Image quality assessment: from error measurement to structural similarity. IEEE Trans. Image Process. 13(4), 600–612 (2004)CrossRefGoogle Scholar
  19. 19.
    Wang, Z., Simoncelli, E., Bovik, A.C.: Multi-scale structural similarity for image quality assessment. In: 37th IEEE Asilomar Conference on Signals, Systems and Computers. Pacific Grove, California (2003)Google Scholar
  20. 20.
    Zhang, F., Li, J., Chen, G., Man, J.: Assessment of color video quality with Singular Value Decomposition of complex matrix. In: 5th International Conference on Information Assurance and Security, pp. 103–106. Xi’an, China (2009)Google Scholar
  21. 21.
    Zhang, L., Zhang, L., Mou, X., Zhang, D.: FSIM: a feature similarity index for image quality assessment. IEEE Trans. Image Process. 20(8), 2378–2386 (2011)CrossRefMathSciNetGoogle Scholar
  22. 22.
    Zhang, L., Zhang, L., Mou, X.: RFSIM: a feature based image quality assessment metric using Riesz transforms. In: 17th IEEE International Conference on Image Processing, pp. 321–324. Hong Kong, China (2010)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  1. 1.Faculty of Electrical Engineering, Department of Signal Processing and Multimedia EngineeringWest Pomeranian University of Technology, SzczecinSzczecinPoland

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