Three-dimensional medical imaging: algorithms and computer systems
ACM Computing Surveys (CSUR)
Vector quantization for volume rendering
VVS '92 Proceedings of the 1992 workshop on Volume visualization
Lossless compression of volume data
VVS '94 Proceedings of the 1994 symposium on Volume visualization
Wavelet-based multiresolutional representation of computational field simulation datasets
VIS '97 Proceedings of the 8th conference on Visualization '97
Integrated volume compression and visualization
VIS '97 Proceedings of the 8th conference on Visualization '97
Representation of Three-Dimensional Digital Images
ACM Computing Surveys (CSUR)
High-quality pre-integrated volume rendering using hardware-accelerated pixel shading
Proceedings of the ACM SIGGRAPH/EUROGRAPHICS workshop on Graphics hardware
Volume Rendering of DCT-Based Compressed 3D Scalar Data
IEEE Transactions on Visualization and Computer Graphics
Volume Data and Wavelet Transforms
IEEE Computer Graphics and Applications
Wavelet Based 3D Compression with Fast Random Access for Very Large Volume Data
PG '99 Proceedings of the 7th Pacific Conference on Computer Graphics and Applications
Fast volume rendering of compressed data
VIS '93 Proceedings of the 4th conference on Visualization '93
Real-time Volume Graphics
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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.