Journal of Software, Vol 6, No 2 (2011), 322-330, Feb 2011
doi:10.4304/jsw.6.2.322-330

A Content-based Classified Hierarchical Vector Quantization Algorithm for Volume Compression

Li-Ping Zhao, Guang-Xue Yue, De-Gui Xiao, Xu Zhou, Xiang Yu, Fei Yu

Abstract


An improved volumetric compression algorithm is presented in this paper. Histogram technique is used for analyzing the trait of volume data. The volume data is then partitioned into volume bricks which  will be classified into two groups, the blocks with meaningless information as one group(also called empty blocks), and those with meaningful information as the other group(also called object blocks). An efficient hierarchical VQ is applied to compress object blocks while for empty blocks, nothing is saved. Compare with analogous Volume Compression algorithm, experimental results demonstrate the proposed algorithm not only can improve the compression rate significantly on the premise of the good quality of reconstruction image, but also can obtain fast decoding speed.



Keywords


Vector quantization, volume Classify, Object blocks, Volume compression, GPU

References


[1] F. Roland, B. Michael, S. Marc. “Sequential Data Compression of Very Large Data in Volume Rendering”. Conference on Vision, Modeling, and Visualization,Saarbrücken,Germany,2007

[2] P. Ning and L. Hesselink, “Fast Volume Rendering of Compressed Data,” IEEE Conference on Visualization, San Jose, CA, 1993

[3] J. Schneider and R. Westermann, “Compression Domain Volume Rendering,” IEEE Conference on Visualization, Seattle, WA, 2003

[4] N. Fout and K. L. Ma, “Transform Coding for Hardware-Accelerated Volume Rendering,” IEEE Transactions on Visualization and Computer Graphics, vol. 13, no. 6, pp. 1600-1607, 2007
doi:10.1109/TVCG.2007.70516
PMid:17968115

[5] L. P. Zhao, D. G. Xiao, and K. L. Li, “An Efficient Algorithm for Large-Scale Volume Data Compression and Its Application in Seismic Data Processing,” in Chinese, Journal of Computer-Aided Design and Computer Graphics, vol. 21, no. 11, 2009

[6] J.Kruger, J. Schneider, R. Westermann. “Compression and rendering of iso-surfaces and point sampled geometry”.The Visual Computer,22(8):517-530,2006
doi:10.1007/s00371-006-0026-2

[7] J.Kruger, J. Schneider, R. Westermann “DUODECIM - a structure for point scan compression and rendering.” IEEE VGTC Symposium Proceedings, Leeds, U. K., 2005

[8] X. Tong, Z. S. Tang. “3D texture hardware assisted volume rendering with space leaping”.in Chinese, Journal of Computers, vol. 21, no.9,1998

[9] J. Krüger, R. Westermann. “Acceleration techniques for GPU-based volume rendering” IEEE Conference on Visualization . Seattle,Washington,2003

[10] S.H. Sun, Z.M. Lu. Technology and application of vector quantization [M]. Beijing: Science Press, 2002(in Chinese)

[11] A. Kaufman and K. Mueller, “Overview of Volume Rendering,” chapter for The Visualization Handbook, C. Johnson and C. Hansen, Eds., Burlington, MA: Academic Press, 2005

[12] Y. Linde, A. Buzo, R M. Gray. “An algorithm for vector quantizer design”.IEEE Transactions on Communications, vol. 28, no. 1,1980
doi:10.1109/TCOM.1980.1094577

[13] H.L. Liao, Z. Ji, Q.H. Wu. “A Novel Genetic Particle-Pair Optimizer for Vector Quantization in Image Coding.” IEEE World Congress on Computational Intelligence, Hong Kong,2008
doi:10.1109/CEC.2008.4630873

[14] H.W.Sun, M. Gu, J.G. Sun, Improved codebook design algorithm based on principal component analysis [J]. Journal of Computer-Aided Design &Computer Graphics, 2005, 17(10): 2245-2250 (in Chinese)

[15] S Scholkopf, S Mika, C.J.C.Burges, et al.Input space versus feature space in kerner-based methods[J].IEEE Transations on Neural Networks,1999,10(5):1000-1017

[16] B. Moghaddam.Principal manifolds and probabilistic subspaces for visual recognition[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2002,24(6):780-788
doi:10.1109/TPAMI.2002.1008384


Full Text: PDF


Journal of Software (JSW, ISSN 1796-217X)

Copyright @ 2006-2012 by ACADEMY PUBLISHER – All rights reserved.