Techniques for Fast Partitioning of Compressed and Uncompressed Video


Video partitioning is the segmentation of a videosequence into visually independent partitions,which represent various identifiable scenes in thevideo. It is an important first step inconsidering other issues in video databasesmanagement, such as indexing and retrieval. Asvideo partitioning is a computationally intensiveprocess, effective management of digital videorequires highly efficient techniques for theprocess. In general, for compressed anduncompressed video, the basic mechanism used toreduce computation is by selective processing of asubpart of the video frames. However, so farthe choice of this proportion has been maderandomly, without any formal basis. An ad hocselection of this subpart cannot always guaranteea reduction in computation while ensuringeffective partitioning.

This paper presents formal methods for determiningthe optimal window size and the minimum thresholdswhich ensure that decisions on scene similarityare made on a reliable, effective and principledbasis. Further, we propose the use ofneighbourhood-based colour ratios, and derive theratio feature for both uncompressed and transformcoded video. The neighbourhood-based ratiofeatures account for both illumination variationand possible motion in the video, while avoidingthe computational burden of explicit motioncompensation procedures. Empirical results showing the performance of the proposed techniques are are also presented.

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  1. 1.

    D.A. Adjeroh and M.C. LeeMechanisms for automatic extraction of primary features for video indexing,” In, Chin et al. (eds.), Lecture Notes in Computer Science: Image Analysis Applications and Computer Graphics, Springer-Verlag: Berlin-Heildelberg, 1995.

    Google Scholar 

  2. 2.

    D.A. Adjeroh and M.C. LeeProbabilistic similarity evaluation using fast incremental matching with optimal premature termination,” Submitted.

  3. 3.

    F. Arman, A. Hsu, and M-Y ChiuImage processing on encoded video sequences,” Multimedia Systems, Vol. 1, pp. 211–219, 1994.

    Google Scholar 

  4. 4.

    S-F Chang and D. G. MesserschmittManipulation and compositing of MC-DCT compressed video,” IEEE Journal of Selected Areas in Communication, Vol. 13, pp. 1–11, 1994.

    Google Scholar 

  5. 5.

    R.J. Clarke, Transform Coding of Images, Academic Press, London, 1985.

    Google Scholar 

  6. 6.

    B.V. Funt and G.D. FinlaysonColor constant color indexing,” IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 17, pp. 522–529, 1995.

    Google Scholar 

  7. 7.

    D. LeGallMPEG: A video compression standard for multimedia applications,” Communications of the ACM, Vol. 34, pp. 46–58, 1991.

    Google Scholar 

  8. 8.

    L.S. Gross and L. W. Ward, Electronic Moviemaking, Wadsworth Publishing Co.: Belmont, California, 1994.

    Google Scholar 

  9. 9.

    A. Hampapur, R. Jain and T. E. WeymouthProduction model based digital video segmentation,” Multimedia Tools and Applications: An International Journal, Vol. 1, pp. 9–46, 1995.

    Google Scholar 

  10. 10.

    M.C. Lee and D. A AdjerohIndexing and retrieval in visual databases via colour ratio histograms,” in Proceedings, 1st International Conference on Visual Information Systems, Melbourne Australia, 1996, pp. 309–316.

  11. 11.

    M. LiouOverview of the px64 kbits/s video coding standard,” Communications of the ACM, Vol. 34, pp. 59–63, 1991.

    Google Scholar 

  12. 12.

    J. Meng, Y. Juan and S-F ChangScene change detection in a MPEG compressed video sequence,” in Proceedings, IS&T/SPIE Conference on Digital Video Compression and Algorithms, pp. 14–25, 1995.

  13. 13.

    A. Nagasaka, and Y. TanakaAutomatic video indexing and full-video search for object appearances,” In, E. Knuth, and L.M. Wegner (eds.), Visual Database Systems II, Elsevier Science Publishers, pp. 113–127, 1992.

  14. 14.

    H. Nicolas and L. LabitMotion and illumination variation estimation using a hierarchy of models: application to image sequence coding,” Journal of Visual Communication and Image Representation, Vol. 6, pp. 303–316, 1995.

    Google Scholar 

  15. 15.

    K. Otsuji, and Y. TonomuraProjection-detecting filter for video cut detection,” Multimedia Systems, Vol. 1, pp. 205–210, 1994.

    Google Scholar 

  16. 16.

    W.B. Pennebaker and J.L Mitchell, JPEG Still Image Data Compression Standard, Van Nostrad Reinhold: New York, 1993.

    Google Scholar 

  17. 17.

    R. Polana and R. NelsonDetecting activities,” Journal of Visual Communication and Image Representation, Vol. 5, pp. 172–180, 1994.

    Google Scholar 

  18. 18.

    K.R. Rao and P. Yip, Discrete Cosine Transform: Algorithms, Advantages, Applications, Academic Press Inc.: Boston, 1990.

    Google Scholar 

  19. 19.

    B.C. Smith and L. A RoweAlgorithms for manipulating compressed images,” IEEE Computer Graphics and Applications, Vol. 13, pp. 34–42, 1993.

    Google Scholar 

  20. 20.

    D. Swanberg, C-F Shu, and R. JainKnowledge guided parsing of video databases,” in Proceecdings, IS&T/SPIE Symposium on Electronic Imaging Science and Technology (Storage and Retrieval for Image and Video Databases II), San Jose, California, pp. 13–24, 1993.

  21. 21.

    Y. TonomuraVideo handling based on structured information for hypermedia systems,” in Proceedings, International Conference on Multimedia Information Systems, Singapore, 1991 pp. 333–344.

  22. 22.

    M.J. Vrhel, H.J. Trussell, and J. BoschDesign and realization of optimal color filters for multi-illuminant color correction,” Journal of Electronic Imaging, Vol. 4, pp. 6–14, 1995.

    Google Scholar 

  23. 23.

    G. K. WallaceThe JPEG still picture compression standard,” Communications of the ACM, Vol. 34, pp. 31–44, 1991.

    Google Scholar 

  24. 24.

    W. Xiong, J. C-M Lee, M-C IpNet comparison: a fast and effective method for classifying image sequences,” in Proceedings, IS&T/SPIE Conference on Storage and Retrieval for Image and Video Databases III, 1995, pp. 318–28.

  25. 25.

    H-J Zhang, A. Kankanhilli, A. and S.W SmoliarAutomatic partitioning of full-motion video,” Multimedia Systems, Vol. 1, pp. 10–28, 1993.

    Google Scholar 

  26. 26.

    H-J Zhang, L.C. Yong, S. W. SmoliarVideo partitioning and browsing using compressed data,” Multimedia Tools and Applications: An International Journal, Vol. 1, pp. 91–111, 1995.

    Google Scholar 

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Adjeroh, D.A., Lee, M. & Orji, C.U. Techniques for Fast Partitioning of Compressed and Uncompressed Video. Multimedia Tools and Applications 4, 225–243 (1997).

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  • video partitioning
  • video indexing
  • compressed video
  • colour ratio features