KSUMM: A Compressed Domain Technique for Video Summarization Using Partial Decoding of Videos

  • Madhushree BasavarajaiahEmail author
  • Priyanka Sharma
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 955)


Generally, the videos are encoded before storing or transmitting. Traditional video processing techniques are compute intensive as they require decoding of the video before processing it. The compressed domain processing of video is an alternative approach where computational overhead is less because a partial decoding is sufficient for many applications. This paper proposes a video summarization technique, KSUMM, that works in the compressed domain. Based on the features extracted from just the I-frames of the video, frames are classified into a predefined number of classes using K-means clustering. Then, the frame which is located at the border of a class in the sequential order is selected to be included in the summary. The length of the summary video can be customized by varying the number of classes during clustering. The quality of the summary was evaluated using Mean Opinion Scores method and the result shows a good Quality of Experience.


Video summarization Machine learning Video abstraction Compressed video processing 


  1. 1.
    Truong, B.T., Venkatesh, S.: Video abstraction: a systematic review and classification. ACM Trans. Multimedia Comput. Commun. Appl. (TOMM) 3(1), 3 (2007)CrossRefGoogle Scholar
  2. 2.
    Babu, R.V., Tom, M., Wadekar, P.: A survey on compressed domain video analysis techniques. Multimedia Tools Appl. 75(2), 1043–1078 (2014)CrossRefGoogle Scholar
  3. 3.
    Kiani, V., Pourreza, H.R.: Flexible soccer video summarization in compressed domain. In: Proceedings of 3rd IEEE International Conference on Computer and Knowledge Engineering, pp. 213–218 (2013)Google Scholar
  4. 4.
    Almeida, J., Leite, N.J., Torres, R.D.S.: Online video summarization on compressed domain. J. Vis. Commun. Image Represent. 24(6), 729–738 (2013)CrossRefGoogle Scholar
  5. 5.
    Almeida, J., Torres, R.D.S., Leite, N.J.: Rapid video summarization on compressed video. In: Proceedings of IEEE International Symposium on Multimedia, pp. 113–120 (2010)Google Scholar
  6. 6.
    Yu, J.C.S., Kankanhalli, M.S., Mulhen, P.: Semantic video summarization in compressed domain MPEG video. In: IEEE International Conference on Multimedia and Expo, Baltimore, MA, USA, 6–9 July, pp. 329–332 (2003)Google Scholar
  7. 7.
    Almeida, J., Leite, N.J., Torres, R.D.S.: VISON: VIdeo Summarization for ONline applications. Pattern Recogn. Lett. 33(4), 397–409 (2012)CrossRefGoogle Scholar
  8. 8.
    Schöffmann, K., Böszörmenyi, L.: Fast segmentation of H.264/AVC bitstreams for on-demand video summarization. In: Satoh, S., Nack, F., Etoh, M. (eds.) MMM 2008. LNCS, vol. 4903, pp. 265–276. Springer, Heidelberg (2008). Scholar
  9. 9.
    Herranz, L., Martínez, J.M.: An integrated approach to summarization and adaptation using H.264/MPEG-4 SVC. Sig. Process. Image Commun. 24(6), 499–509 (2009)CrossRefGoogle Scholar
  10. 10.
    Zhong, R., Hu, R., Wang, Z., Wang, S.: Fast synopsis for moving objects using compressed video. IEEE Sig. Process. Lett. 21(7), 834–838 (2014)CrossRefGoogle Scholar
  11. 11.
    Pedregosa, et al.: Scikit-learn: machine learning in Python. JMLR 12, 2825–2830 (2011)MathSciNetzbMATHGoogle Scholar
  12. 12.
    De Avila, S.E.F., Lopes, A.P.B., da Luz Jr., A., de Albuquerque Araújo, A.: VSUMM a mechanism designed to produce static video summaries and a novel evaluation method. Pattern Recogn. Lett. 32(1), 56–68 (2011)CrossRefGoogle Scholar
  13. 13.
    Babu, R.V., Tom, M., Wadekar, P.: A survey on compressed domain video analysis techniques. Multimedia Tools Appl. 75(2), 1043–1078 (2016)CrossRefGoogle Scholar
  14. 14.
    Oh, S., et al.: A large-scale benchmark dataset for event recognition in surveillance video. In: IEEE conference on Computer Vision and Pattern Recognition (CVPR), pp. 3153–3160 (2011)Google Scholar

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© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.Department of Computer Engineering, Institute of TechnologyNirma UniversityAhmedabadIndia

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