Video Data Compression Methods in the Decision Support Systems

  • V. Barannik
  • O. Yudin
  • Y. Boiko
  • R. Ziubina
  • N. Vyshnevska
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 754)


The paper presents the developed methods of video data compression and decompression providing the maximum degree of intersecting information flows of critical video information at the given quality levels of digital video images. Due to the developed methods, mathematical models and techniques, the technology of video data compression has been improved on the basis of reducing the structural redundancy under limited loss of visualization quality. The proposed technology provides an increased level of effective functioning of communication channels and critical video information processing, as well as presents an opportunity for information support and improved quality of decision-making in crisis situations.


DSS-systems Video data compression Image compression Structural redundancy Psycho-visual redundancy Adaptive coding Discrete cosine transformation 


  1. 1.
    Rees, L.P., Deane, J.K., Rakes, T.R., Baker, W.H.: Decision support for cybersecurity risk planning. Decis. Support Syst. 51(3), 493–505 (2011)CrossRefGoogle Scholar
  2. 2.
    Lai, C.L., Chen, Y.S.: The application of intelligent system to digital image forensics. In: 2009 International Conference on Machine Learning and Cybernetics, vol. 5, pp. 2991–2998. IEEE (2009)Google Scholar
  3. 3.
    Yudin, O., Boiko, Y., Frolov, O.: Organization of decision support systems for crisis management. In: Problems of Infocommunications Science and Technology (PIC S&T), 2015 Second International Scientific-Practical Conference, pp. 115–117. IEEE (2015)Google Scholar
  4. 4.
    Tank, D.M.: Enable better and timelier decision-making using real-time business intelligence system. Int. J. Inf. Eng. Electron. Bus. (IJIEEB) 7(1), 43–48 (2015). CrossRefGoogle Scholar
  5. 5.
    Kumari, N., Agarwal, A.: A review on wireless data center management. Int. J. Comput. Appl. 138(13) (2016)CrossRefGoogle Scholar
  6. 6.
    Cuzzocrea, A., Song, I.Y., Davis, K.C.: Analytics over large-scale multidimensional data: the big data revolution! In: Proceedings of the ACM 14th International Workshop on Data Warehousing and OLAP, pp. 101–104. ACM (2011)Google Scholar
  7. 7.
    Shang, J., Ding, W., Shi, Y., Sun, Y.: Fast intra mode decision algorithm based on texture direction detection for H.264/AVC. Int. J. Educ. Manage. Eng. (IJEME) 1(5), 70–77 (2011). Scholar
  8. 8.
    Lakhno, V., Boiko, Y., Akhmetov, B., Mishchenko, A.: Designing a decision support system for the weakly formalized problems in the provision of cybersecurity. Eastern Eur. J. Enterpr. Technol. 1(2(85)), 4–15 (2017).
  9. 9.
    Garae, J., Ko, R.K.L.: Visualization and data provenance trends in decision support for cybersecurity. In: Palomares Carrascosa, I., Kalutarage, H., Huang, Y. (eds.) Data Analytics and Decision Support for Cybersecurity, Data Analytics, pp. 243–270. Springer, Cham (2017). Scholar
  10. 10.
    Ziubina, R., Boiko, Yu.: Video data compression methods in the aviation crisis management. Inżynier XXI wieku: VI Międzynarodowa Konferencja Studentów oraz Doktorantów. 73 (2016)Google Scholar
  11. 11.
    Jaiswal, S., Dhavale, S.: Video forensics in temporal domain using machine learning techniques. Int. J. Comput. Netw. Inf. Secur. (IJCNIS) 5(9), 58–67 (2013). Scholar
  12. 12.
    Kaushik, M.: Comparative analysis of exhaustive search algorithm with ARPS algorithm for motion estimation. Int. J. Appl. Inf. Syst. (IJAIS) 1(6), 16–19 (2012)Google Scholar
  13. 13.
    Nayak, A., Biswal, B., Sabut, S.K.: Evaluation and comparison of motion estimation algorithms for video compression. Int. J. Image Graph. Signal Process. (IJIGSP) 5(10), 9–18 (2013). Scholar
  14. 14.
    Jeengar, V., Omkar, S.N., Singh, A., Yadav, M.K., Keshri, S.: A review comparison of wavelet and cosine image transforms. IJIGSP 4(11), 16–25 (2012). Scholar
  15. 15.
    Wang, W., Farid, H.: Exposing digital forgeries in video by detecting double MPEG compression. In: Proceedings of the 8th Workshop on Multimedia and Security, pp. 3–47. ACM (2006)Google Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2019

Authors and Affiliations

  • V. Barannik
    • 1
  • O. Yudin
    • 1
    • 2
  • Y. Boiko
    • 1
    • 2
  • R. Ziubina
    • 1
    • 2
  • N. Vyshnevska
    • 2
  1. 1.Kharkiv National Air Force UniversityKharkivUkraine
  2. 2.National Aviation UniversityKyivUkraine

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