Lossless compression of map contours by context tree modeling of chain codes

We consider lossless compression of digital contours in map images. The problem is attacked by the use of context-based statistical modeling and entropy coding of the chain codes. We propose to generate an optimal n-ary incomplete context tree by first constructing a complete tree up to a predefined depth and creating the optimal tree by pruning out nodes that do not provide improvement in compression. We apply this method for both vector and raster maps. Experiments show that the proposed method gives lower bit rates than the existing methods of chain codes compression for the set of test data.