Context tree compression of multi-component map images

Abstract

We consider compression of multi-component map images by context modeling and arithmetic coding. We apply an optimized multi-level context tree for modeling the individual binary layers. The context pixels can be located within a search area in the current layer, or in a reference layer that has already been compressed. The binary layers are compressed using an optimized processing sequence that makes maximal utilization of the inter-layer dependencies. The structure of the context tree is a static variable depth binary tree, and the context information is stored only in the leaves of the tree. The proposed technique achieves an improvement of about 25% over a static 16 pixel context template, and 15% over a similar single-level context tree.