This paper introduces a sparse signal representation algorithm in redundant dictionaries, called the M-Term Pursuit (MTP), with an application to image representation and scalable coding. The MTP algorithm belongs to the framework of the matching pursuit (MP); it expands the image into a linear combination of atoms, selected from a large collection of spatial atoms. The MTP relies on the concept of dictionary partitioning, i.e., as splitting the dictionary into $L$ disjoint sub-dictionaries, each carrying some specific information. Then, it iteratively finds a $K$-term approximation, by selecting $M$ atoms at a time, where $M