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Pyramid coder with nonlinear prediction

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Ideal VLC coder for each level of the pyramid. Criteria. Global compression rate of the pyramid ... quantization (variable bit-allocation and non-optimal VLC) ... – PowerPoint PPT presentation

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Title: Pyramid coder with nonlinear prediction


1
Pyramid coder with nonlinear prediction
Laurent Meunier Antoine Manens
2
Framework
  • No quantization lossless coding
  • Open-loop Closed-loop
  • Ideal VLC coder for each level of the pyramid

3
Criteria
  • Global compression rate of the pyramid
  • SNR and visual quality of the partially
    reconstructed pictures
  • Cost of the decoding process

4
Review of linear techniques
  • Haar
  • Gaussian filters(Burt Adelson, 1983)
  • Ideal filters
  • Optimal filters for piecewise polynomial fitting
    (Chin, Choi, Luo, 1992)
  • Splines (Unser, Aldroubi, Eden, 1993)
  • Efficient, but introduces blurring and aliasing

5
Review of non-linear techniques
  • Multi-level median filter (Defee, Neuvo, 1991)
  • Anisotropic pyramid (You, Kaveh,1996)
  • Improvement can be obtained on specific visual
    patterns like edges
  • More complicated to analyse.
  • Reduce and Expand Filters chosen from
    intuition/experiments, no guarantee of optimality.


6
Optimal NL interpolation
  • Hyp Decimation filter is given
  • Problem find 4 predictors for the even-even,
    odd-even, even-odd and odd-odd pixels.
  • Optimal solution conditional expected value of
    the pixel given its neighbourhood for each
    predictor.
  • The implementation requires to reduce the number
    of possible neighbourhoods
  • gt Partition the image using features
    likeaverage intensity, gradient, presence of
    edges, texture.

7
Implementation of the optimal NL filter
  • Example image obtained with ? 3 features
    (avg intensity, grad/x, grad/y) ? 8
    levels of quantization ? 8x8x8 512 cells
  • Pretty coarse because only one intensity per
    cell.
  • Solution Use an optimal linear predictor that
    takes the local best fitting plane instead of the
    expected value.
  • Train the predictor using a set of images.

8
Hybrid Method
  • Motivation some methods do a better job than
    the others in some kind of neighborhoods

Implementation the algorithm switches technique
depending on the type of neighborhood. Use a
training set to learn decision table.
9
Method mapping
10
Visual comparison
Original
BurtAdelson with a 0.6
Cubic interpolation
Optimal non-linear
11
Numerical results
Entropies
  • Lena 7.44
  • Burt(0.6) 5.69
  • Spline(3) 5.61
  • Cubic interpolation 5.43
  • Approx. opt. NL 5.39
  • MMF 5.35
  • DPCM 5.03

12
Conclusion
  • Significant improvements over the BurtAdelson
    pyramid were achieved both in terms of
    compression rate and of SNR of the partially
    reconstructed images
  • Rate reduction is lower than with DPCM. The
    lossless algorithm should therefore be used only
    where progressive transmission is necessary.
  • More thorough study of the feature choice and of
    the number of bins for the proposed NL technique
    is necessary.
  • Further study should include the issue of
    quantization (variable bit-allocation and
    non-optimal VLC)
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