Adaptation of the Combined Image Similarity Index for Video Sequences

  • Krzysztof Okarma
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 233)


One of the most relevant areas of research in the image analysis domain is the development of automatic image quality assessment methods which should be consistent with human perception of various distortions. During last years several metrics have been proposed as well as their combinations which lead to highly linear correlation with subjective opinions. One of the recently proposed ideas is the Combined Image Similarity Index which is the nonlinear combination of three metrics outperforming most of currently known ones for major image datasets. In this paper the applicability and extension of this metric for video quality assessment purposes is analysed and the obtained performance results are compared with some other metrics using the video quality assessment database recently developed at École Polytechnique Fédérale de Lausanne and Politecnico di Milano for quality monitoring over IP networks, known as EPFL-PoliMI dataset.


Video Sequence Video Quality Mean Opinion Score Image Quality Assessment Subjective Score 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Wang, Z., Bovik, A.: A universal image quality index. IEEE Signal Proc. Letters 9(3), 81–84 (2002)CrossRefGoogle Scholar
  2. 2.
    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
  3. 3.
    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
  4. 4.
    Forczmański, P., Furman, M.: Comparative Analysis of Benchmark Datasets for Face Recognition Algorithms Verification. In: Bolc, L., Tadeusiewicz, R., Chmielewski, L.J., Wojciechowski, K. (eds.) ICCVG 2012. LNCS, vol. 7594, pp. 354–362. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  5. 5.
    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)Google Scholar
  6. 6.
    Larson, E., Chandler, D.: Most apparent distortion: full-reference image quality assessment and the role of strategy. Journal of Electronic Imaging 19(1), 011006 (2010)Google Scholar
  7. 7.
    Sheikh, H., Wang, Z., Cormack, L., Bovik, A.: LIVE image quality assessment database release 2 (2005),
  8. 8.
    Seshadrinathan, K., Soundararajan, R., Bovik, A., Cormack, L.: Study of Subjective and Objective Quality Assessment of Video. IEEE Trans. Image Proc. 19(6), 1427–1441 (2010)MathSciNetCrossRefGoogle Scholar
  9. 9.
    Moorthy, A., Choi, L., de Veciana, G., Bovik, A.: Mobile Video Quality Assessment Database. In: Proc. IEEE ICC Workshop on Realizing Advanced Video Optimized Wireless Networks (2012)Google Scholar
  10. 10.
    Moorthy, A., Choi, L., Bovik, A., de Veciana, G.: Video Quality Assessment on Mobile Devices: Subjective, Behavioral and Objective Studies. IEEE J. Selected Topics in Signal Proc. 6(6), 652–671 (2012)CrossRefGoogle Scholar
  11. 11.
    De Simone, F., Tagliasacchi, M., Naccari, M., Tubaro, S., Ebrahimi, T.: A H.264/AVC video database for the evaluation of quality metrics. In: Proc. IEEE Int. Conf. Acoustics Speech Signal Processing, pp. 2430–2433 (2010)Google Scholar
  12. 12.
    Sheikh, H., 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
  13. 13.
    Sheikh, H., Bovik, A.: Image information and visual quality. IEEE Trans. Image Proc. 15(2), 430–444 (2006)CrossRefGoogle Scholar
  14. 14.
    Zhang, L., Zhang, L., Mou, X., Zhang, D.: FSIM: A Feature Similarity index for image quality assessment. IEEE Trans. Image Proc. 20(8), 2378–2386 (2011)MathSciNetCrossRefGoogle Scholar
  15. 15.
    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, Part I. LNCS (LNAI), vol. 6113, pp. 539–546. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  16. 16.
    Okarma, K.: Video quality assessment using the combined full-reference approach. In: Choraś, R.S. (ed.) Image Processing and Communications Challenges 2. AISC, vol. 84, pp. 51–58. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  17. 17.
    Okarma, K.: Combined Image Similarity Index. Optical Review 19(5), 349–354 (2009)CrossRefGoogle 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

Personalised recommendations