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Literature Survey on Wavelet Compression of Still Color Images

  1. Orthonormal Wavelets With Balanced Uncertainty
  2. Monro, D.M., Bassil, B.E. and Dickson, G.J.
    Proc. IEEE ICIP 1996, Vol. 2, pp. 581-584
    This paper tries to determine the "best" wavelet by using the psychovisual evaluation of selected images. The wavelets under consideration are modified to trade off between time and frequency uncertainty. The authors note that by selecting a nearly balanced ratio between the two produces the best subjective results for image compression.

  3. A Fast Wavelet Based Karhunen-Loeve Transform
  4. Greenshields, I.R. and J.A. Rosiene
    Pattern Recognition, 31, 7 1998
    The Karhuhen-Loeve Transform is computationally expensive, but provides the best basis system for representing information. To avoid this complexity, many "fast" algorithms make potentially incorrect assumptions about the statistics of the data. In this paper, the authors take advantage of some of the processing done by the wavelet decomposition to avoid the high cost of computing the full KLT.

  5. Optimized Wavelets for Fingerprint Compression
  6. Sherlock, B.G. and Monro, D.M.
    Proc. IEEE ICASSP 1996, Vol. 3, pp. 1447-1450
    This paper discusses how orthonormal wavelets can be optimized for finger printing. The basic technique is an iterative process of analysis and synthesis followed by rms error comparison. Psychovisual testing was performed which supported the rms error between the original and recovered images.

  7. Space-Frequency Balance in Biorthogonal Wavelets
  8. D.M. Monro and B.G. Sherlock
    Proc. IEEE ICIP 1997, Vol. 1, pp. 624-627
    Wavelets can be optimized by deciding between space and frequency uncertainty. The authors derive a metric that suggests the optimal trade-off point. The goal is a fast iterative process that can produce a FIR filter bank that will satisfy their metric.

  9. On the Space of Orthonormal Wavelets
  10. B.G. Sherlock and D.M. Monro
    IEEE Transactions on Signal Processing, June 1998, Vol. 46, No. 6, pp. 1716-1720
    This paper demonstrates how one can compute othornomal wavelets recursively. The goal is to be able to produce all wavelets in a given two-dimensional space without repetition so that they may be tested exhaustively. Source code is provided in this paper that will produce the FIR coefficients for any even length filter bank.

  11. Approximate Continuous Wavelet Transform with an Application to Noise Reduction
  12. J. M. Lewis and C. S. Burrus
    Proceedings of ICASSP 1998
    This paper looks at the characteristics of the wavelets that may be selected when computing the DWT. In particular, the Daubechies family of wavelets are examined and the coefficients are adapted. The modifications to the coefficients that the author makes are for noise reduction and edge detection.

  13. Smooth Biorthogonal Wavelets for Applications in Image Compression
  14. J. E. Odegard and C. S. Burrus
    Workshop, Loen, Norway, September 1996
    This paper looks at a new class of biorthogonal wavelet filters. The authors show the standard equation that must be solved in order to generate a biorthogonal wavelet. They then show by variation of the vanishing moments, that a smoother filter may be created.

  15. Discrete Finite Variation: A New Measure of Smoothness for the Design of Wavelet Basis
  16. J. E. Odegard and C. S. Burrus
    Proceedings of ICASSP, Atlanta, GA, May 1996
    This paper recognizes that smooth biorthogonal wavelets are important for image compression. Since artifacts are introduced primarily by the non smooth portions of the wavelet it is important to make the wavelets as smooth as possible. The authors of this paper propose a metric in an effort to determine the "best" wavelet.

  17. Design of Linear Phase Cosine Modulated Filter Banks for Subband Image Compression
  18. J. E. Odegard, R. A. Gopinath, C. S. Burrus
    Rice University CML Technical Report CML TR94-06
    The authors of this paper focus on the three basic artifacts with image compression, blocking, blurring, and ringing. Shorter or longer length filter banks are discussed as tradeoffs between the two common wavelet artifacts, blurring and ringing. This paper provides only a quick overview to research that is further discussed below.

  19. Wavelet-Based Signal Processing Using Hidden Markov Models
  20. M. S. Crouse, R. D. Nowak, and R. G. Baraniuk
    To appear in IEEE Transactions on Signal Processing (Special Issue on Wavelets and Filterbanks), 1998
    In this paper, the authors discuss the clustering and persistence characteristics of wavelets. They look at the coefficients that result from wavelets as being modeled by Guassian and nonGaussian statistics. The concept of a Hidden Markov Model (HMM) allows the authors to further exploit the interdependency between coefficients than the simple quadtree structure that Shapiro originally recognized.

  21. Improved Wavelet Denoising via Empirical Wiener Filtering
  22. S. Ghael, A. M. Sayeed, and R. G. Baraniuk
    Proc. SPIE, San Diego, July 1997
    This paper looks at wavelets in the signal processing domain. Some of the goals with this use of wavelets include removing noise from signals. The example the authors offer is a noisy Doppler test signal which they recover by wavelet filtering.

  23. Wavelet Based SAR Speckle Reduction and Image Compression
  24. J. E. Odegard, H. Guo, M. Lang, C. S. Burrus, R. O. Wells Jr., L. M. Novak and M. Hiett
    SPIE Symposium on OE/Aerospace Sensing and Dual Use Photonics, Algorithm for Synthetic Aperture Radar Imagery II, 17-21 April 1995, Orlando, FL., Also a Rice University Tech. Report CML TR95-08
    This paper looks at compressing and enhancing SAR images. Typical images are huge and need to be transmitted back down to Earth, so compression is essential. The authors note that after wavelet compression, the resulting image will be despeckled and therefore more highly correlated. With this higher correlation, it is possible to further compress the image before transmitting.

  25. Wavelet-Based Post-Processing of Low Bit Rate Transform Coded Images
  26. R. Gopinath, M. Lang, H. Guo, J. E. Odegard
    IEEE Proc. ICIP, Nov. 1994, Also a Rice University CML Technical Report CML TR94-15
    JPEG decompression may introduce blocking artifacts into the resulting image if the bit rate is too low. To correct for this, a wavelet filter may be passed over the image after processing to help smooth out the artifacts. This paper focuses on the problems with under- and over-compensating resulting in blocky or overly smoothed images.

  27. Enhancement of Decompressed Images at Low Bit Rates
  28. R. Gopinath, M. Lang, H. Guo, J. E. Odegard
    SPIE, Mathematical Imaging: Wavelet Applications in Signal Processing and Image Processing II, Vol. 2303, Jul. 1994, Also a Rice University CML Technical Report CML TR94-05
    In this paper, the authors examine the problem of blocking in the DCT. They review a variety of techniques to remove artifacts from the image including a wavelet technique. This post-processing effect on decompressed JPEG images helps reduce the blocking effect while maintaining high definition lines in the image.

  29. A Simple Scheme for Adapting Time-Frequency Representations
  30. D. L. Jones, R. G. Baraniuk
    IEEE Transactions on Signal Processing, Vol. 42, No. 12, pp. 3530-3535, December 1994
    The Heisenberg uncertainty between time and frequency can be optimally reduced through extensive knowledge of the signal to be encoded. However, this is computationally prohibitive, so efficient methods of decided where to trade off are needed. The authors of this paper propose a solution that uses a single free parameter to facilitate the analysis of the tradeoffs.
Last Modified: December 13, 1998 - Barry E. Mapen