An improved quadtree-based algorithm for lossless compression of volumetric datasets

In this paper a novel algorithm for lossless compression of volumetric data is presented. This algorithm is based on our previously presented algorithm for lossless compression of volumetric data, which uses quadtree encoding of slices of data for discovering the coherence and similarities between consecutive slices. By exploiting these properties of the data, the algorithm can efficiently compress volumetric datasets. In this paper we upgrade the basic algorithm by introducing several new routines for determination of coherence and similarities between slices, as well as some new entropy encoding techniques. With this approach, we managed to additionally improve the compression ratio of the algorithm. Presented algorithm has two significant properties. Firstly, it is designed for lossless compression of volumetric data, which is not the case with most of existing algorithms for compression of voxel data, but this is a very important feature in some fields, i.e. medicine. Secondly, the algorithm supports progressive reconstruction of volumetric data and is therefore appropriate for visualization of compressed volumetric datasets over the internet. Key-Words: Volumetric Data, Voxels, Compression, Lossless Compression, Quadtree Encoding