Generalized Lifting (GL) has been studied for lossy image
compression in [2,3]. It has been demonstrated that the method
leads to a significant reduction of the wavelet coefficients energy
and entropy. The definition of the GL relies on an estimation of the
pdf of the pixel to encode conditioned to a surrounding context.
The objective of this paper is to present an improved method for
the estimation of the pdf at the local level. Rather than assuming
that the local pdf is monomodal, symmetric, and centered at the
central value of the best context match within a neighborhood, as
in , we follow the idea of self similarity proposed in  for
denoising, and propose to estimate the pdf using all the causal
contexts within a window. Therefore, all the available knowledge
about the neighborhood is incorporated. No assumptions about the
characteristics of the pdf are made. A generalized lifting operator
that minimizes the energy is applied to each context during the
encoding process. Experimental results show an important
increment in the energy and entropy gains when compared to
previous strategies [2,3].
CitationRolon, J.C.; Salembier, P. Improved local pdf estimation in the wavelet domain for generalized lifting. A: Picture Coding Symposium. "PCS 2010". Chicago: 2010.
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