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Compression of Patient Monitoring Video Using Motion Segmentation Technique

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Abstract

The volume of patient monitoring video acquired in hospitals is very huge and hence there is a need for better compression of the same for effective storage and transmission. This paper presents a new motion segmentation technique, which improves the compression of patient monitoring video. The proposed motion segmentation technique makes use of a binary mask, which is obtained by thresholding the standard deviation values of the pixels along the temporal axis. Two compression methods, which make use of the proposed motion segmentation technique, are presented. The first method uses MPEG-4 coder and 9/7-biorthogonal wavelet for compressing the moving and stationary portions of the video respectively. The second method uses 5/3-biorthogonal wavelet for compressing both the moving and the stationary portions of the video. The performances of these compression algorithms are evaluated in terms of PSNR and bitrate. From the experimental results, it is found that the proposed motion technique improves the performance of the MPEG-4 coder. Among the two compression methods presented, the MPEG-4 based method performs better for bitrates less than 767 Kbps whereas for bitrates above 767 Kbps the performance of the wavelet based method is found superior.

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References

  1. Richardson, I. E. G., H.264 and MPEG-4 Video Compression—Video Coding for Next-generation Multimedia. John Wiley & Sons, 2003.

  2. Watkinson, J., The MPEG Handbook, Focal Press, 2001.

  3. Lin, K. K., and Gray, R. M., Wavelet video coding with dependent optimization. IEEE Trans. Circuits Syst. Video Technol. 14(4):542–553, 2004.

    Article  Google Scholar 

  4. Secker, A., and Taubman, D., Lifting-based invertible motion adaptive transform (LIMAT) framework for highly scalable video compression. IEEE Trans. Image Process 12(12):1530–1542, 2003.

    Article  Google Scholar 

  5. Chao H., Jianyu D., Zheng, Y. F., and Zhigang, G., Optimal 3-D coefficient tree structure for 3-D wavelet video coding. IEEE Trans. Circuits Syst. Video Technol. 13(10):961–972, 2003.

    Article  Google Scholar 

  6. Karayiannis, N. B., and Li, Y., A replenishment technique for low bit-rate video compression based on wavelets and vector quantization. IEEE Trans. Circuits Syst. Video Technol. 11(5):658–663, 2001.

    Article  Google Scholar 

  7. Levy, I. K., and Wilson, R., Three-dimensional wavelet transform video coding using symmetric codebook vector quantization. IEEE Trans. Image Process. 10(3):470–475, 2001

    Article  MATH  MathSciNet  Google Scholar 

  8. Liu, Q., Sclabassi, R. J., Scheuer, M. L., and Sun, M., A two-step method for compression of medical monitoring video. IEEE Conf. Eng. Med. Biol. Soc. 1:845–848, 2003.

    Google Scholar 

  9. Shamim, A., and Robinson, J. A., Object-based video coding by global-to-local motion segmentation. IEEE Trans. Circuits Syst. Video Technol. 12(12):1106–1116, 2002.

    Article  Google Scholar 

  10. Jinhui, P., Lin, C.-W., Gu, C., and Sun, M.-T., A robust video object segmentation scheme with prestored background information. IEEE Symp. Circuits Syst. 3:803–806, 2002.

    Google Scholar 

  11. Huang, C.-L., and Liao, B.-Y., A robust scene-change detection method for video segmentation. IEEE Trans. Circuits Syst. Video Technol. 11(12):1281–1288, 2001.

    Article  Google Scholar 

  12. Chang, Y. D., Lee, D. G., Ryoo, S. Y., and Lee, D. S., Detection of patient movement for video EEG monitoring. IEEE Int. Conf. Eng. Med. Biol. 2:959–962, 1998.

    Google Scholar 

  13. Meier, T., and Ngan, K. N., Segmentation and tracking of moving objects for content-based video coding. IEEE Trans. Circuits Syst. Video Technol. 9(8):1190–1203, 1999.

    Article  Google Scholar 

  14. Liu, Q., Sun, M., and Sclabassi, R. J., Illumination-invariant change detection model for patient monitoring video. Conf. Eng. Med. Biol. Soc. EMBC 2004, 1:1782–1785, 2004.

    Google Scholar 

  15. Tsaig, Y., and Averbuch, A., Automatic segmentation of moving objects in video sequences: a region labeling approach. IEEE Trans. Circuits Syst. Video Technol. 12(7):597–612, 2002.

    Article  Google Scholar 

  16. Mester, R., Aach, T., and Dümbgen, L., Illumination-invariant change detection using a statistical colinearity criterion. In: Radig, B., and Florczyk, S. (Eds.) Pattern Recognition: Proc. of 23rd DAGM Symposium, Sept 2001 Lecture Notes in Computer Science, Springer Verlag, Munich/Germany, pp. 170–177.

  17. Kim, M., Choi, J. G., and Kim, D., etc., A VOP generation tool: Automatic segmentation of moving objects in image sequences based on spatiotemporal information. IEEE Trans. Circuits Syst. Video Technol. 9(8):1216–1226, 1999.

    Article  Google Scholar 

  18. Neri, S., Colonnese, G. R., and Talone, P., Automatic moving object and background separation. Signal Process. 66:219–232, 1998.

    Google Scholar 

  19. Lim, S. J., Two-Dimensional Signal and Image Processing. Englewood Cliffs, NJ, Prentice Hall, pp. 469–476, 1990

  20. Gonzales, R. C., and Woods, R. E., Digital Image Processing. Addison Wesley, 1998.

  21. Barua, S., Kotteri, K. A., Bell, A. E., and Carletta, J. E., Optimal quantized lifting coefficients for the 9/7 wavelet [image compression applications]. IEEE Int. Conf. Acoustics Speech Signal Process. 5:193–196, 2004.

    Google Scholar 

  22. Said, A., and Pearlman, W. A., A new, fast, and efficient image codec based on set partitioning in hierarchical trees. IEEE Trans. Circuits Syst. Video Technol. 6(3):243–250, 1996.

    Article  Google Scholar 

  23. Fowler, J. E., QccPack: An open-source software library for quantization, compression, and coding. Proc. SPIE Applications of Digital Image Processing XXIII, A. G. Tescher, Ed. San Diego, CA, vol. 4115, pp. 294–301, 2000.

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Acknowledgement

This research work has been funded by the Ministry of Science, Technology and Environment (MOSTE), Malaysia (IRPA Project number: 04-99-01-0052-EA04815).

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Authors and Affiliations

  1. Center for Multimedia Computing, Faculty of Information Technology, Multimedia University, 63100, Cyberjaya, Malaysia

    R. Shyamsunder, C. Eswaran & N. Sriraam

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  1. R. Shyamsunder
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  2. C. Eswaran
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  3. N. Sriraam
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Correspondence to R. Shyamsunder.

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Shyamsunder, R., Eswaran, C. & Sriraam, N. Compression of Patient Monitoring Video Using Motion Segmentation Technique. J Med Syst 31, 109–116 (2007). https://doi.org/10.1007/s10916-006-9036-x

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  • DOI: https://doi.org/10.1007/s10916-006-9036-x

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