  In this paper, we propose a novel scheme for scalable image coding based on
the concept of epitome. An epitome can be seen as a factorized representation
of an image. Focusing on spatial scalability, the enhancement layer of the
proposed scheme contains only the epitome of the input image. The pixels of the
enhancement layer not contained in the epitome are then restored using two
approaches inspired from local learning-based super-resolution methods. In the
first method, a locally linear embedding model is learned on base layer patches
and then applied to the corresponding epitome patches to reconstruct the
enhancement layer. The second approach learns linear mappings between pairs of
co-located base layer and epitome patches. Experiments have shown that
significant improvement of the rate-distortion performances can be achieved
compared to an SHVC reference.
