Generalized Lifting with adaptive local pdf estimation for image coding

Abstract

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.

Improved local pdf estimation in the wavelet domain for Generalized Lifting

“…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]. …”

“…The resulting mapping can be implemented by a subtraction. It is shown in [3] that the adaptive local pdf estimation approach produces results comparable to those of [2].…”

“…In [3], a local pdf estimation approach has been proposed. The focus is on the local characteristics of the sample to encode and its context.…”

“…In order to design the mapping under this assumption, we only need to know the value of the pdf maximum. An adaptive estimation strategy that consists in searching in a causal neighborhood of the current sample to encode for contexts that are similar to the one being analyzed was proposed in [3]. The idea is that it is very likely that the most similar context has the most similar central value that minimizes the energy of the detail signal.…”

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ABSTRACTGeneralized 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.

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