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A Complete Image Compression Scheme Based on Overlapped Block Transform with Post-Processing

  • C. KwanEmail author
  • B. Li
  • R. Xu
  • X. Li
  • T. Tran
  • T. Nguyen
Open Access
Research Article

Abstract

A complete system was built for high-performance image compression based on overlapped block transform. Extensive simulations and comparative studies were carried out for still image compression including benchmark images (Lena and Barbara), synthetic aperture radar (SAR) images, and color images. We have achieved consistently better results than three commercial products in the market (a Summus wavelet codec, a baseline JPEG codec, and a JPEG-2000 codec) for most images that we used in this study. Included in the system are two post-processing techniques based on morphological and median filters for enhancing the perceptual quality of the reconstructed images. The proposed system also supports the enhancement of a small region of interest within an image, which is of interest in various applications such as target recognition and medical diagnosis

Keywords

Reconstructed Image Color Image Median Filter Medical Diagnosis Synthetic Aperture Radar 

References

  1. 1.
    Kwan C, Li B, Xu R, et al.: SAR image compression using wavelets. Wavelet Applications VIII, 2001, Proceedings of SPIE 4391: 349–357.Google Scholar
  2. 2.
    Pennebaker WB, Mitchell JL: JPEG: Still Image Compression Standard. Van Nostrand Reinhold, New York, NY, USA; 1993.Google Scholar
  3. 3.
    Malvar HS: Signal Processing with Lapped Transforms. Artech House, Norwood, Mass, USA; 1992.zbMATHGoogle Scholar
  4. 4.
    Malvar HS: Biorthogonal and nonuniform lapped transforms for transform coding with reduced blocking and ringing artifacts. IEEE Transactions on Signal Processing 1998, 46: 1043–1053. 10.1109/78.668555CrossRefGoogle Scholar
  5. 5.
    Reeves HC, Lim JS: Reduction of blocking effects in image coding. Optical Engineering 1984, 23(1):34–37.Google Scholar
  6. 6.
    de Queiroz RL, Nguyen T, Rao KR: GenLOT: generalized linear-phase lapped orthogonal transform. IEEE Transactions on Signal Processing 1996, 44(3):497–507. 10.1109/78.489023CrossRefGoogle Scholar
  7. 7.
    Tran T, Nguyen T:OnOpen image in new window-channel linear phase FIR filter banks and application in image compression. IEEE Transactions on Signal Processing 1997, 45(9):2175–2187. 10.1109/78.622942CrossRefGoogle Scholar
  8. 8.
    Xiong Z, Ramchandran K, Orchard MT: Space-frequency quantization for wavelet image coding. IEEE Transactions on Image Processing 1997, 6(5):677–693. 10.1109/83.568925CrossRefGoogle Scholar
  9. 9.
    Princen JP, Johnson AW, Bradley AB: Subband/transform coding using filter bank designs based on time domain aliasing cancellation. Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP '87), April 1987, Dallas, Tex, USA 2161–2164.CrossRefGoogle Scholar
  10. 10.
    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
  11. 11.
    Said A, Pearlman WA: A new fast and efficient image codec on set partitioning in hierarchical trees. IEEE Transactions on Circuits and Systems for Video Technology 1996, 6: 243–250. 10.1109/76.499834CrossRefGoogle Scholar
  12. 12.
    Compression with reversible embedded wavelets RICOH Company Ltd. submission to ISO/IEC JTC1/SC29/WG1 for the JTC1.29.12 work item, 1995. Can be obtained on the World Wide Web, https://doi.org/www.crc.ricoh.com/CREW
  13. 13.
    Vaidyanathan PP: Multirate Systems and Filter Banks. Prentice-Hall, Englewood Cliffs, NJ, USA; 1993.zbMATHGoogle Scholar
  14. 14.
    Strang G, Nguyen T: Wavelets and Filter Banks. Wellesley-Cambridge Press, Wellesley, Mass, USA; 1996.zbMATHGoogle Scholar
  15. 15.
    Vetterli M, Kovačević J: Wavelets and Subband Coding. Prentice-Hall, Englewood Cliffs, NJ, USA; 1995.zbMATHGoogle Scholar
  16. 16.
    DeVore RA, Jawerth B, Lucier BJ: Image compression through wavelet transform coding. IEEE Transactions on Information Theory 1992, 38(2, part II):719–746. 10.1109/18.119733MathSciNetCrossRefGoogle Scholar
  17. 17.
    Daubechies I: Ten Lectures on Wavelets, CBMS Conference Series. SIAM, Philadelphia, Pa, USA; 1992.CrossRefGoogle Scholar
  18. 18.
    Law NF, Siu WC: Successive structural analysis using wavelet transform for blocking artifacts suppression. Signal Processing 2001, 81(7):1373–1387. 10.1016/S0165-1684(01)00018-4CrossRefGoogle Scholar
  19. 19.
    Zakhor A: Iterative procedures for reduction of blocking effects in transform image coding. IEEE Transactions on Circuits and Systems for Video Technology 1992, 2(1):91–95. 10.1109/76.134377CrossRefGoogle Scholar
  20. 20.
    Yang Y, Galatsanos NP, Katsaggelos AK: Regularized reconstruction to reduce blocking artifacts of block discrete cosine transform compressed images. IEEE Transactions on Circuits and Systems for Video Technology 1993, 3(6):421–432. 10.1109/76.260198CrossRefGoogle Scholar
  21. 21.
    Yang Y, Galatsanos NP: Projection-based spatially adaptive reconstruction of block-transform compressed images. IEEE Transactions on Image Processing 1995, 4(7):896–908. 10.1109/83.392332CrossRefGoogle Scholar
  22. 22.
    Kim SD, Yi J, Kim HM, Ra JB: A deblocking filter with two separate mode in block-based video coding. IEEE Transactions on Circuits and Systems for Video Technology 1999, 9(2):156–160.Google Scholar
  23. 23.
    Park HW, Lee YL: A post-processing method for reducing quantization effects in low bit-rate moving picture coding. IEEE Transactions on Circuits and Systems for Video Technology 1999, 9(2):161–171.CrossRefGoogle Scholar
  24. 24.
    Liew AW-C, Yan H: Blocking artifacts suppression in blockcoded images using overcomplete wavelet representation. IEEE Transactions on Circuits and Systems for Video Technology 2004, 14(4):450–461. 10.1109/TCSVT.2004.825555CrossRefGoogle Scholar
  25. 25.
    Weerasinghe C, Liew AW-C, Yan H: Artifact reduction in compressed images based on region homogeneity constraints using the projections onto convex sets algorithm. IEEE Transactions on Circuits and Systems for Video Technology 2002, 12(10):891–897. 10.1109/TCSVT.2002.804881CrossRefGoogle Scholar

Copyright information

© Kwan et al. 2006

Authors and Affiliations

  • C. Kwan
    • 1
    Email author
  • B. Li
    • 2
  • R. Xu
    • 1
  • X. Li
    • 1
  • T. Tran
    • 3
  • T. Nguyen
    • 4
  1. 1.Intelligent Automation, Inc. (IAI)RockvilleUSA
  2. 2.Department of Computer Science and Engineering, Ira. A. Fulton School of EngineeringArizona State UniversityTempeUSA
  3. 3.Department of Electrical and Computer Engineering, The Whiting School of EngineeringThe Johns Hopkins UniversityBaltimoreUSA
  4. 4.Department of Electrical and Computer Engineering, Jacobs School of EngineeringUniversity of CaliforniaSan Diego, La JollaUSA

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