A Packetized SPIHT Algorithm with Overcomplete Wavelet Coefficients for Increased Robustness

  • Y SrirajaEmail author
  • Tanja Karp
Open Access
Research Article
Part of the following topical collections:
  1. Frames and Overcomplete Representations in Signal Processing, Communications, and Information Theory


This paper presents a wavelet-based image encoding scheme with error resilience and error concealment suitable for transmission over networks prone to packet losses. The scheme involves partitioning the data into independent descriptions of roughly equal lengths, achieved by a combination of packetization and modifications to the wavelet tree structure without additional redundancy. With a weighted-averaging-based interpolation method, our proposed encoding scheme attains an improvement of about 0.5–1.5 dB in PSNR over other similar methods. We also investigate the use of overcomplete wavelet transform coefficients as side information for our encoding scheme to improve the error resilience when severe packet losses occur. Experiments show that we are able to achieve a high coding performance along with a good perceptual quality for the reconstructed image.


Packet Loss Interpolation Method Encode Scheme Wavelet Coefficient Code Performance 


  1. 1.
    Goyal VK: Multiple description coding: compression meets the network. IEEE Signal Processing Magazine 2001, 18(5):74–93. 10.1109/79.952806CrossRefGoogle Scholar
  2. 2.
    Vaishampayan VA: Design of multiple description scalar quantizers. IEEE Transactions on Information Theory 1993, 39(3):821–834. 10.1109/18.256491MathSciNetCrossRefGoogle Scholar
  3. 3.
    Servetto SD, Ramachandran K, Vaishampayan VA, Nahrstedt K: Multiple description wavelet based image coding. IEEE Transactions on Image Processing 2000, 9(5):813–826. 10.1109/83.841528CrossRefGoogle Scholar
  4. 4.
    Srinivasan M, Chellappa R: Multiple description subband coding. Proceedings of IEEE International Conference on Image Processing (ICIP '98), October 1998, Chicago, Ill, USA 684–688.Google Scholar
  5. 5.
    Mohr AE, Riskin EA, Ladner RE: Unequal loss protection: graceful degradation of image quality overpacket erasure channels through forward error correction. IEEE Journal on Selected Areas in Communications 2000, 18(6):819–828. 10.1109/49.848236CrossRefGoogle Scholar
  6. 6.
    Shapiro JM: Embedded image coding using zerotrees of wavelet coefficients. IEEE Transactions on Signal Processing 1993, 41(12):3445–3462. 10.1109/78.258085CrossRefGoogle Scholar
  7. 7.
    Said A, Pearlman WA: A new, fast, and efficient image codec based on set partitioning in hierarchical trees. IEEE Transactions on Circuits and Systems for Video Technology 1996, 6(3):243–250. 10.1109/76.499834CrossRefGoogle Scholar
  8. 8.
    Creusere CD: A new method of robust image compression based on the embedded zerotree wavelet algorithm. IEEE Transactions on Image Processing 1997, 6(10):1436–1442. 10.1109/83.624967CrossRefGoogle Scholar
  9. 9.
    Rogers JK, Cosman PC: Robust wavelet zerotree image compression with fixed-length packetization. Proceedings of Data Compression Conference (DCC '98), March–April 1998, Snowbird, Utah, USA 418–427.Google Scholar
  10. 10.
    Bajic IV, Woods JW: Domain-based multiple description coding of images and video. IEEE Transactions on Image Processing 2003, 12(10):1211–1225. 10.1109/TIP.2003.817248CrossRefGoogle Scholar
  11. 11.
    DeBrunner VE, DeBrunner L, Wang L, Radhakrishnan S: Error control and concealment for image transmission. IEEE Communications Society Surveys and Tutorials 2000., 3(1):CrossRefGoogle Scholar
  12. 12.
    Kim T, Choi S, Van Dyck RE, Bose NK: Classified zerotree wavelet image coding and adaptive packetization for low-bit-rate transport. IEEE Transactions on Circuits and Systems for Video Technology 2001, 11(9):1022–1034. 10.1109/76.946519CrossRefGoogle Scholar
  13. 13.
    Rane SD, Remus J, Sapiro G: Wavelet-domain reconstruction of lost blocks in wireless image transmission and packet-switched networks. Proceedings of IEEE International Conference on Image Processing (ICIP '02), September 2002, Rochester, NY, USA 1: I-309–I-312.CrossRefGoogle Scholar
  14. 14.
    Vetterli M, Kovačević J: Wavelets and Subband Coding. Prentice-Hall, Englewood Cliffs, NJ, USA; 1995.zbMATHGoogle Scholar
  15. 15.
    Strang G, Nguyen T: Wavelets and Filter Banks. Wellesley-Cambridge Press, Wellesley, Mass, USA; 1996.zbMATHGoogle Scholar
  16. 16.
    Fliege NJ: Multirate Digital Signal Processing: Multirate Systems, Filter Banks, Wavelets. John Wiley & Sons, Chichester, UK; 1994.zbMATHGoogle Scholar
  17. 17.
    Li X: New results of phase shifting in the wavelet space. IEEE Signal Processing Letters 2003, 10(7):193–195. 10.1109/LSP.2003.811587CrossRefGoogle Scholar
  18. 18.
    Wang Y, Orchard MT, Vaishampayan VA, Reibman AR: Multiple description coding using pairwise correlating transforms. IEEE Transactions on Image Processing 2001, 10(3):351–366. 10.1109/83.908500CrossRefGoogle Scholar

Copyright information

© Y. Sriraja and T. Karp 2006

This article is published under license to BioMed Central Ltd. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Authors and Affiliations

  1. 1.Department of Electrical and Computer EngineeringTexas Tech UniversityLubbockUSA

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