High flexibility scalable image coding

This paper presents a new, highly flexible, scalable image coder based on a Matching Pursuit expansion. The dictionary of atoms is built by translation, rotation and anisotropic refinement of gaussian functions, in order to efficiently capture edges in natural images. In the same time, the dictionary is invariant under isotropic scaling, which interestingly leads to very simple spatial resizing operations. It is shown that the proposed scheme compares to state-of-the-art coders when the compressed image is transcoded to a lower (octave-based) spatial resolution. In contrary to common compression formats, our bit-stream can moreover easily and efficiently be decoded at any spatial resolution, even with irrational re-scaling factors. In the same time, the Matching Pursuit algorithm provides an intrinsically progressive stream. This worthy feature allows for easy rate filtering operations, where the least important atoms are simply discarded to fit restrictive bandwidth constraints. Our scheme is finally shown to favorably compare to state-of-the-art progressive coders for moderate to quite important rate reductions.