Lossless Volume Data Compression Schemes

Volumetric data is one of the most frequently generated type of data in modern medical imaging. Technical advances in the respective scanning technologies also increase the amount of data that is generated by a typical scanner. Since 1971 with the introduction of the first CT scanner, the space requirements for representing data have increased rapidly, in essence at an exponential rate. In this paper, we examine various compression methods for their applicability for volume data, focusing on the reduced space requirements of the approaches. We apply these methods to a wide range of 8bit and 10-12bit volume datasets, thus exposing strengths and weaknesses of the compression methods. In summary, significant differences in compression performances clearly indicate which compression techniques should be used.

[1]  Michael Garland,et al.  Multiresolution Modeling: Survey and Future Opportunities , 1999, Eurographics.

[2]  Michael Wand,et al.  A hardware architecture for multi-resolution volume rendering , 2005, HWWS '05.

[3]  Kwan-Liu Ma,et al.  High-Quality Rendering of Compressed Volume Data Formats , 2005, EuroVis.

[4]  Insung Ihm,et al.  Wavelet‐Based 3D Compression Scheme for Interactive Visualization of Very Large Volume Data , 1999, Comput. Graph. Forum.

[5]  Gabriel Taubin,et al.  3D Geometry Compression and Progressive Transmission , 1999 .

[6]  David A. Huffman,et al.  A method for the construction of minimum-redundancy codes , 1952, Proceedings of the IRE.

[7]  S. Sahni,et al.  State of the Art Lossless Image Compression Algorithms , 2007 .

[8]  Renato Pajarola,et al.  Out-Of-Core Algorithms for Scientific Visualization and Computer Graphics , 2002 .

[9]  Wayne O. Cochran,et al.  Fractal Volume Compression , 1996, IEEE Trans. Vis. Comput. Graph..

[10]  Wolfgang Straßer,et al.  Real-time decompression and visualization of animated volume data , 2001, Proceedings Visualization, 2001. VIS '01..

[11]  Jürgen Abel Grundlagen des Burrows-Wheeler-Kompressionsalgorithmus , 2003, Informatik Forschung und Entwicklung.

[12]  M. Nelson Data compression with the Burrows-Wheeler Transform , 1996 .

[13]  Terry A. Welch,et al.  A Technique for High-Performance Data Compression , 1984, Computer.

[14]  Rüdiger Westermann,et al.  Compression domain rendering of time-resolved volume data , 1995, Proceedings Visualization '95.

[15]  Eduard Gröller,et al.  Optimal regular volume sampling , 2001, Proceedings Visualization, 2001. VIS '01..

[16]  M. Levoy,et al.  Fast volume rendering using a shear-warp factorization of the viewing transformation , 1994, SIGGRAPH.

[17]  Ian H. Witten,et al.  Arithmetic coding revisited , 1998, TOIS.

[18]  Ronald G. Driggers,et al.  Encyclopedia of optical engineering , 2003 .

[19]  Jens Schneider,et al.  Compression domain volume rendering , 2003, IEEE Visualization, 2003. VIS 2003..

[20]  Abraham Lempel,et al.  A universal algorithm for sequential data compression , 1977, IEEE Trans. Inf. Theory.

[21]  Boon-Lock Yeo,et al.  Volume Rendering of DCT-Based Compressed 3D Scalar Data , 1995, IEEE Trans. Vis. Comput. Graph..

[22]  D. J. Wheeler,et al.  A Block-sorting Lossless Data Compression Algorithm , 1994 .

[23]  Y. Fisher Fractal Image Encoding and Analysis , 1998 .

[24]  Shigeru Muraki,et al.  Volume data and wavelet transforms , 1993, IEEE Computer Graphics and Applications.

[25]  James E. Fowler,et al.  Lossless compression of volume data , 1994, VVS '94.

[26]  Arie E. Kaufman,et al.  Towards a comprehensive volume visualization system , 1992, Proceedings Visualization '92.