Optimized lifting schemes based on ENO stencils for image approximation

In this paper, we propose to improve the classical lifting-based wavelet transforms by defining three classes of pixels which will be predicted differently. More specifically, the proposed idea is inspired by the Essentially Non-Oscillatory (ENO) transform and consists in shifting the stencil used f...

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Bibliographic Details
Published in2015 IEEE International Conference on Image Processing (ICIP) pp. 4308 - 4312
Main Authors Kaaniche, Mounir, Matei, Basarab, Meignen, Sylvain
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.09.2015
Subjects
Adaptive wavelets
ENO prediction
filter optimization
image approximation
Image resolution
Lifting scheme
Linear approximation
Optimization
Polynomials
Wavelet transforms
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Summary:In this paper, we propose to improve the classical lifting-based wavelet transforms by defining three classes of pixels which will be predicted differently. More specifically, the proposed idea is inspired by the Essentially Non-Oscillatory (ENO) transform and consists in shifting the stencil used for prediction in order to reduce the error near image singularities. Moreover, the different filters associated with these classes will be optimized in order to design a multiresolution representation well adapted to image characteristics. Our simulations show that the resulting multiscale representation leads to much lower amplitudes of the detail coefficients and improves the linear approximation properties.
DOI:10.1109/ICIP.2015.7351619