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Progressive and Error-Resilient Transmission Strategies for VLC Encoded Signals over Noisy Channels

  • Hervé JégouEmail author
  • Christine Guillemot
Open Access
Research Article
  • 776 Downloads
Part of the following topical collections:
  1. Video Analysis and Coding for Robust Transmission

Abstract

This paper addresses the issue of robust and progressive transmission of signals (e.g., images, video) encoded with variable length codes (VLCs) over error-prone channels. This paper first describes bitstream construction methods offering good properties in terms of error resilience and progressivity. In contrast with related algorithms described in the literature, all proposed methods have a linear complexity as the sequence length increases. The applicability of soft-input soft-output (SISO) and turbo decoding principles to resulting bitstream structures is investigated. In addition to error resilience, the amenability of the bitstream construction methods to progressive decoding is considered. The problem of code design for achieving good performance in terms of error resilience and progressive decoding with these transmission strategies is then addressed. The VLC code has to be such that the symbol energy is mainly concentrated on the first bits of the symbol representation (i.e., on the first transitions of the corresponding codetree). Simulation results reveal high performance in terms of symbol error rate (SER) and mean-square reconstruction error (MSE). These error-resilience and progressivity properties are obtained without any penalty in compression efficiency. Codes with such properties are of strong interest for the binarization of Open image in new window -ary sources in state-of-the-art image, and video coding systems making use of, for example, the EBCOT or CABAC algorithms. A prior statistical analysis of the signal allows the construction of the appropriate binarization code.

Keywords

Transmission Strategy Symbol Error Rate Turbo Decode Variable Length Code Error Resilience 

References

  1. 1.
    Maxted J, Robinson J: Error recovery for variable length codes. IEEE Transactions on Information Theory 1985, 31(6):794–801. 10.1109/TIT.1985.1057110MathSciNetCrossRefGoogle Scholar
  2. 2.
    Ferguson T, Rabinowitz JH: Self-synchronizing Huffman codes. IEEE Transactions on Information Theory 1984, 30(4):687–693. 10.1109/TIT.1984.1056931MathSciNetCrossRefGoogle Scholar
  3. 3.
    Lam W-M, Reibman AR: Self-synchronizing variable-length codes for image transmission. Proceedings of IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP '92), March 1992, San Francisco, Calif, USA 3: 477–480.Google Scholar
  4. 4.
    Takishima Y, Wada M, Murakami H: Reversible variable length codes. IEEE Transactions on Communications 1995, 43(234):158–162.CrossRefGoogle Scholar
  5. 5.
    Wen J, Villasenor JD: Reversible variable length codes for efficient and robust image and video coding. Proceedings of Data Compression Conference (DCC '98), March–April 1998, Snowbird, Utah, USA 471–480.Google Scholar
  6. 6.
    Bauer R, Hagenauer J: Iterative source/channel-decoding using reversible variable length codes. Proceedings of Data Compression Conference (DCC '00), March 2000, Snowbird, Utah, USA 93–102.CrossRefGoogle Scholar
  7. 7.
    Murad AH, Fuja TE: Joint source-channel decoding of variable-length encoded sources. Proceedings of IEEE Information Theory Workshop, June 1998, Killarney, Ireland 94–95.Google Scholar
  8. 8.
    Demir N, Sayood K: Joint source/channel coding for variable length codes. Proceedings of Data Compression Conference (DCC '98), March–April 1998, Snowbird, Utah, USA 139–148.Google Scholar
  9. 9.
    Redmill DW, Kingsbury NG: The EREC: an error-resilient technique for coding variable-length blocks of data. IEEE Transactions on Image Processing 1996, 5(4):565–574. 10.1109/83.491333CrossRefGoogle Scholar
  10. 10.
    Wang Y, Wenger S, Wen J, Katsaggelos AK: Error resilient video coding techniques. IEEE Signal Processing Magazine 2000, 17(4):61–82. 10.1109/79.855913CrossRefGoogle Scholar
  11. 11.
    Hagenauer J: Rate-compatible punctured convolutional codes (RCPC codes) and their applications. IEEE Transactions on Communications 1988, 36(4):389–400. 10.1109/26.2763CrossRefGoogle Scholar
  12. 12.
    Cho S, Pearlman WA: Multilayered protection of embedded video bitstreams over binary symmetric and packet erasure channels. Journal of Visual Communication and Image Representation 2005, 16(3):359–378. 10.1016/j.jvcir.2004.08.001CrossRefGoogle Scholar
  13. 13.
    Huffman D: A method for construction of minimum redundancy codes. Proceedings of IRE 1952, 40(9):1098–1101.CrossRefGoogle Scholar
  14. 14.
    Hu TC, Tucker AC: Optimal computer search trees and variable-length alphabetical codes. SIAM Journal on Applied Mathematics 1971, 21(4):514–532. 10.1137/0121057MathSciNetCrossRefGoogle Scholar
  15. 15.
    Taubman D: High performance scalable image compression with EBCOT. IEEE Transactions on Image Processing 2000, 9(7):1158–1170. 10.1109/83.847830CrossRefGoogle Scholar
  16. 16.
    Marpe D, Schwarz H, Wiegand T: Context-based adaptive binary arithmetic coding in the H.264/AVC video compression standard. IEEE Transactions on Circuits and Systems for Video Technology 2003, 13(7):620–636. 10.1109/TCSVT.2003.815173CrossRefGoogle Scholar
  17. 17.
    Zhou G, Zhang Z: Synchronization recovery of variable-length codes. IEEE Transactions on Information Theory 2002, 48(1):219–227. 10.1109/18.971750MathSciNetCrossRefGoogle Scholar
  18. 18.
    Bahl L, Cocke J, Jelinek F, Raviv J: Optimal decoding of linear codes for minimizing symbol error rate. IEEE Transactions on Information Theory 1974, 20(2):284–287.MathSciNetCrossRefGoogle Scholar
  19. 19.
    Bauer R, Hagenauer J: Turbo-FEC/VLC-decoding and its application to text compression. Proceedings of the 34th Conference on Information Sciences and systems (CISS '00), March 2000, Princeton, NJ, USA WA6–WA11.Google Scholar
  20. 20.
    Guyader A, Fabre E, Guillemot C, Robert M: Joint source-channel turbo decoding of entropy-coded sources. IEEE Journal on Selected Areas in Communications 2001, 19(9):1680–1696. 10.1109/49.947033CrossRefGoogle Scholar
  21. 21.
    Balakirsky VB: Joint source-channel coding with variable length codes. Proceedings of IEEE International Symposium on Information Theory (ISIT '97) , June-July 1997, Ulm, Germany 419.CrossRefGoogle Scholar
  22. 22.
    Zeger K, Gersho A: Pseudo-Gray coding. IEEE Transactions on Communications 1990, 38(12):2147–2158. 10.1109/26.64657CrossRefGoogle Scholar
  23. 23.
    Farvardin N: A study of vector quantization for noisy channels. IEEE Transactions on Information Theory 1990, 36(4):799–809. 10.1109/18.53739MathSciNetCrossRefGoogle Scholar
  24. 24.
    Levenshtein VI: Binary codes capable of correcting deletions, insertions and reversals. Soviet Physics Doklady 1966, 10(8):707–710.MathSciNetGoogle Scholar
  25. 25.
    Jégou HSource code: C++ implementation of the for proposed algorithms. https://doi.org/www.irisa.fr/temics/Equipe/Jegou/src/source-code.php

Copyright information

© Jégou and Guillemot 2006

Authors and Affiliations

  1. 1.IRISA, Université de RennesRennesFrance
  2. 2.INRIA Rennes IRISARennesFrance

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