Parallel Parsing of MPEG Video in a Multi-threaded Multiprocessor Environment

  • Suchendra M. Bhandarkar
  • Shankar R. Chandrasekaran
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1800)


Video parsing refers to the detection of scene changes and special effects in the video stream and is used to extract key frames from a video stream. In this paper, we propose parallel algorithms for the detection of scene changes and special effects in MPEG video in a multi-threaded multiprocessor environment. The parallel video parsing algorithms are capable of detecting abrupt scene changes (cuts), gradual scene changes (dissolves) and dominant camera motion in the form of pans and zooms in an MPEG1-coded video stream while entailing minimal decompression of the MPEG1 video stream. Experimental results on real video clips in a multi-threaded multiprocessor environment are presented. These algorithms are useful when real-time parsing of streaming video or high-throughput parsing of archival video are desired.


Motion Vector Video Stream Work Process MPEG1 Video Scene Change 
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.
    S.M. Bhandarkar and A. A. Khombadia, Motion-based parsing of compressed video, Proc IEEE Intl. Wkshp. Multimedia Database Mgmt. Sys., Dayton, Ohio, August 5–7, 1998, pp 80–87.Google Scholar
  2. 2.
    S.M. Bhandarkar, Y.S. Warke and A.A. Khombadia, Integrated parsing of compressed video, Proc. Intl. Conf. Visual Inf. Mgmt. Sys., Amsterdam, The Netherlands, June 2–4, 1999, pp 269–276.Google Scholar
  3. 3.
    V. Bhaskaran and K. Konstantinides, Image and Video Compression Standards: Algorithms and Architectures, Kluwer Academic Publishers, 1995, pp 161–194.Google Scholar
  4. 4.
    A. Bilas, J. Fritts and J.P. Singh, Real-time parallel MPEG-2 decoding in software, Technical Report 516-96, Department of Computer Science, Princeton University, March 1996.Google Scholar
  5. 5.
    C.W. Ngo, T.C. Pong and R.T. Chin, Detection of gradual transitions through temporal slice analysis, Proc. IEEE Conf. Computer Vision and Pattern Recognition, Fort Collins, Colorado, June 23–25, 1999, pp 36–41.Google Scholar
  6. 6.
    B.L. Yeo and B. Liu, Rapid scene analysis on compressed video, IEEE Trans. Cir. and Sys. for Video Tech., Vol. 5(6), 1995, pp 533–544.CrossRefGoogle Scholar
  7. 7.
    H.J. Zhang, C.Y. Low, and S.W. Smoliar, Video parsing and browsing using compressed data, Jour. Multimedia Tools Appl., Vol. 1(1), 1995, pp. 89–111.CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2000

Authors and Affiliations

  • Suchendra M. Bhandarkar
    • 1
  • Shankar R. Chandrasekaran
    • 1
  1. 1.Department of Computer ScienceThe University of GeorgiaAthensUSA

Personalised recommendations