Pyramid coder with nonlinear prediction
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Pyramid coder with nonlinear prediction. Laurent Meunier Antoine Manens. Framework. No quantization : lossless coding Open-loop = Closed-loop Ideal VLC coder for each level of the pyramid. Criteria. Global compression rate of the pyramid

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Pyramid coder with nonlinear prediction
Pyramid coder with nonlinear prediction

Laurent Meunier

Antoine Manens


Framework
Framework

  • No quantization : lossless coding

  • Open-loop = Closed-loop

  • Ideal VLC coder for each level of the pyramid


Criteria
Criteria

  • Global compression rate of the pyramid

  • SNR and visual quality of the partially reconstructed pictures

  • Cost of the decoding process


Review of linear techniques
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


Review of non linear techniques

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.

Review of non-linear techniques

  • Multi-level median filter (Defee, Neuvo, 1991)

  • Anisotropic pyramid (You, Kaveh,1996)


Optimal nl interpolation
Optimal NL interpolation edges

  • 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

  • => Partition the image using features likeaverage intensity, gradient, presence of edges, texture.


Implementation of the optimal nl filter
Implementation of the optimal NL filter edges

  • 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.


Hybrid method
Hybrid Method edges

  • 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.



Visual comparison
Visual comparison edges

Original

Burt&Adelson with a = 0.6

Cubic interpolation

Optimal non-linear


Numerical results
Numerical results edges

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


Conclusion
Conclusion edges

  • Significant improvements over the Burt&Adelson 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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