In this paper we introduce an adaptive local pdf estimation strategy for the construction of Generalized Lifting (GL) mappings in the wavelet domain. Our approach consists in trying to estimate the local pdf of the wavelet coefficients conditioned to a context formed by neighboring coefficients. To this end, we search in a small causal window for similar contexts. This strategy is independent of the wavelet filters used to transform the image. Experimental results exhibit interesting gains in terms of energy reduction comparable to those obtained in [8]. In order to take benefit from this energy reduction, specific entropy encoder should be designed in the future.

This paper introduces the design of context-based models of contours in the wavelet domain, which are used to construct generalized lifting (GL) mappings for image compression. The GL context-based mapping may significantly reduce the signal energy and the resulting bitrate. Here, we propose a strategy to define a reduced set of structured models to design the GL. The models capture the contour structures and are contrast-invariant. Initial experimental results applying the strategy on a wavelet subband exhibit potential gains. Iterations of the GL scheme as well as an adaptive entropy coding strategy may increase the coding gain.

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