Interleaved S+P Pyramidal Decomposition with Refined Prediction Model

Marie Babel (1) , Olivier Déforges (1) , Joseph Ronsin (1)

1 IETR - Institut d'Electronique et de Télécommunications de Rennes

Scalability and others functionalities such as the Region of Interest encoding become essential properties of an efficient image coding scheme. Within the framework of lossless compression techniques, S+P and CALIC represent the state-of-the-art. The proposed Interleaved S+P algorithm outperforms these method while providing the desired properties. Based on the LAR (Locally Adaptive Resolution) method, an original pyramidal decomposition combined with a DPCM scheme is elaborated. This solution uses the S-transform in such a manner that a refined prediction context is available for each estimation steps. The image coding is done in two main steps, so that the first one supplies a LAR low-resolution image of good visual quality, and the second one allows a lossless reconstruction. The method exploits an implicit context modelling, intrinsic property of our content-based quad-tree like representation.

Domains: Signal and Image processing; Signal and Image Processing

Marie Babel, Olivier Déforges, Joseph Ronsin. Interleaved S+P Pyramidal Decomposition with Refined Prediction Model. Sep 2005, pp.750-753. ⟨hal-00132112⟩