Image Recovery from Compressed Video Using Multichannel Regularization

  • Yongyi Yang
  • Mungi Choi
  • Nikolas P. Galatsanos
Chapter

Abstract

In this chapter we propose a multichannel recovery approach to ameliorate coding artifacts in compressed video. The main shortcomings of previously proposed recovery algorithms for this problem was that only spatial smoothness was explicitly enforced. In this chapter we attempt to ameliorate the above problem. According to the proposed approach, prior knowledge is enforced by introducing regularization operators which complement the transmitted data. The term multichannel implies that both the spatial (within-channel) and temporal (across-channel) properties of the image sequences are used in the recovery process. More specifically, regularization operators are defined that in addition to spatial smoothness explicitly enforce smoothness along the motion trajectories. Since the compressed images due to quantization belong to known convex sets, iterative gradient projection algorithms are proposed to minimize the regularized functional and simultaneously guarantee membership to these sets. Numerical experiments are shown using H.261 and H.263 coded sequences. These experiments demonstrate that introduction of temporal regularization offers a significant improvement both visually and from a peak signal-to-noise ratio point of view.

Keywords

Video Sequence Motion Vector Video Code Regularization Term Predictive Code 
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.

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References

  1. [1]
    W. B. Pennebaker and J. L. Mitchel, JPEG: Still Image Data Compression Standard, Van Nostrand Reinhold, 1993.Google Scholar
  2. [2]
    ISO/IEC 11172–2, “Information Technology — Coding of Moving Pictures and Associated Audio for Digital Storage Media up to about 1.5 Mbits/s — Video,” Geneva, 1993.Google Scholar
  3. [3]
    ISO/IEC 13818–2, “Information technology — Generic coding of moving pictures and associated audio: Video,” Nov. 1994.Google Scholar
  4. [4]
    ITU-T Recommendation H.261, Video Codec for Audiovisual Services at p x 64 kbits. Google Scholar
  5. [5]
    ITU-T Recommendation H.263, Video Coding for low bitrate communication,Sept. 1997.Google Scholar
  6. [6]
    Thomas Sikora, “MPEG digital video-coding standard,” IEEE Signal Processing Magazine, Vol. 15, No. 5, pp. 82–100, Sept. 1997.CrossRefGoogle Scholar
  7. [7]
    B. Ramamurthi and A. Gersho, “Nonlinear space-variant postprocessing of block coded images,” IEEE Trans. on Acoust., Speech and Signal Processing, Vol. 34, No. 5, pp. 1258–1267, October 1986.CrossRefGoogle Scholar
  8. [8]
    N. R. Galatsanos and A. K. Katsaggelos, “Methods for choosing the regularization parameter and estimating the noise variance in image restoration and their relation”, IEEE Trans. on Image Processing, Vol. 1, No. 3, pp. 322–336, J uly, 1992Google Scholar
  9. [9]
    K. Sauer, “Enhancement of low bit-rate coded images using edge detection and estimation”, Computer Vision Graphics and Image Processing: Graphical Models and Image Processing, Vol. 53, No. 1, pp. 52–62, January 1991.MATHCrossRefGoogle Scholar
  10. [10]
    S. Minami and A. Zakhor, `An optimization approach for removing blocking effects in transform coding,“ IEEE Trans on Circuits and Systems for Video Tech., Vol. 5, No. 2, pp. 74–82, April 1995.CrossRefGoogle Scholar
  11. [11]
    C. Kuo, and R. Hsieh, “Adaptive postprocessor for block encoded images,” IEEE Trans on Circuits and Systems for Video Tech., Vol. 5, No. 4, pp. 298–304, August 1995.CrossRefGoogle Scholar
  12. [12]
    Y. L. Lee, H. C. Kim, and H. W. Park, “Blocking effect reduction of JPEG images by signal adaptive filtering,” IEEE Trans. on Image Processing„ vol. 7, no. 2, Feb. 1998.Google Scholar
  13. [13]
    R. Rosenholtz and A. Zakhor, “Iterative procedures for reduction of blocking effects in transform image coding”, IEEE Trans on Circuits and Systems for Video Tech., Vol. 2, No. 1, pp. 91–94, March 1992.Google Scholar
  14. [14]
    Mungi Choi and N. P. Galatsanos, “Multichannel regularized iterative restoration of motion compensated image sequences,” Journal of Visual Communication and Image Representation, Vol. 7, No. 3, pp. 244–258, Sept. 1996.Google Scholar
  15. [15]
    Y. Yang, N. Galatsanos and A. Katsaggelos, “Regularized reconstruction to reduce blocking artifacts of block discrete cosine transform compressed images,” IEEE Trans on Circuits and Sys. for Video Tech., Vol. 3, No. 6, pp. 421–432, Dec. 1993.Google Scholar
  16. [16]
    Y. Yang, N. Galatsanos and A. Katsaggelos, “Projection-based spatially adaptive image reconstruction of block transform, compressed images” IEEE Trans. on Image Processing, Vol. 4, No. 7, pp. 896–908, July 1995.Google Scholar
  17. [17]
    T. Ozcelik, J. Brailean, and A. K. Katsaggelos, “Image and video compression algorithms based on recovery techniques using mean field annealing,” IEEE Proceedings, Vol. 83, No. 2, pp. 304–316, February 1995.Google Scholar
  18. [18]
    T. O’Rourke and R. Stevenson, “Improved image decompression for reduced transform coding artifacts,” IEEE Trans. on Circuits and Sys. for Video Tech., Vol. 4, No. 6, pp. 490–499, Dec. 1995.CrossRefGoogle Scholar
  19. [19]
    J. Luo, C. Chen, K. Parker, and T. S. Huang, `Artifact reduction in low bit rate DCT-based image compression,“ IEEE Trans. on Image Processing, vol. 5, pp. 1363–1368, Spet. 1996.Google Scholar
  20. [20]
    Y. Yang and N. Galatsanos, “Removal of compression artifacts using projections onto convex sets and line process modeling”, IEEE Trans on Image Processing, Vol. 6, No. 10, pp. 1345–1357, Oct. 1997.CrossRefGoogle Scholar
  21. [21]
    M. G. Choi, Y. Yang and N. P. Galatsanos, “Multichannel regularized recovery of compressed video,” Int. Conf. on Image Processing, pp. 271–274, Santa Barbara, Nov. 1997.Google Scholar
  22. [22]
    A. Tikhonov and V. Arsenin, Solution of Ill-Posed Problems, New York: Wiley, 1977.Google Scholar
  23. [23]
    J. M. Ortega and W. C. Reinbolt, Iterative Solutions to Nonlinear Equations in Several Variables, New York, Academic, 1970.Google Scholar

Copyright information

© Springer Science+Business Media Dordrecht 1998

Authors and Affiliations

  • Yongyi Yang
    • 1
  • Mungi Choi
    • 1
  • Nikolas P. Galatsanos
    • 1
  1. 1.Department of Electrical and Computer EngineeringIllinois Institute of TechnologyChicagoUSA

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