This Article 
   
 Share 
   
 Bibliographic References 
   
 Add to: 
 
Digg
Furl
Spurl
Blink
Simpy
Google
Del.icio.us
Y!MyWeb
 
 Search 
   
Transform Coding for Hardware-accelerated Volume Rendering
November/December 2007 (vol. 13 no. 6)
pp. 1600-1607
Hardware-accelerated volume rendering using the GPU is now the standard approach for real-time volume rendering, although limited graphics memory can present a problem when rendering large volume data sets. Volumetric compression in which the decompression is coupled to rendering has been shown to be an effective solution to this problem; however, most existing techniques were developed in the context of software volume rendering, and all but the simplest approaches are prohibitive in a real-time hardware-accelerated volume rendering context. In this paper we present a novel block-based transform coding scheme designed specifically with real-time volume rendering in mind, such that the decompression is fast without sacrificing compression quality. This is made possible by consolidating the inverse transform with dequantization in such a way as to allow most of the reprojection to be precomputed. Furthermore, we take advantage of the freedom afforded by off-line compression in order to optimize the encoding as much as possible while hiding this complexity from the decoder. In this context we develop a new block classification scheme which allows us to preserve perceptually important features in the compression. The result of this work is an asymmetric transform coding scheme that allows very large volumes to be compressed and then decompressed in real-time while rendering on the GPU.

[1] S. Adlersberg and V. Cuperman, Transform domain vector quantization for speech signals. In International Conference on Acoustics, Speech, and Signal Processing, volume 12, pages 1938–1941, Apr. 1987.
[2] K. Aizawa, H. Harashimia, and H. Miyakawa, Adaptive discrete cosine transform coding with vector quantization for color images. In International Conference on Acoustics, Speech, and Signal Processing, volume 11, pages 985–988, Apr. 1986.
[3] A. Binotto, J. Comba, and C. Freitas, Real-time volume rendering of time-varying data using a fragment-shader compression approach. In IEEE Symposium on Parallel and Large-Data Visualization and Graphics, pages 69–75, 2003.
[4] W. Cochran, J. Hart, and P. Flynn, Fractal volume compression. IEEE Transactions on Visualization and Computer Graphics, 2 (4): 313–322, Dec. 1996.
[5] W. Equitz, A new vector quantization clustering algorithm. IEEE Transactions on Acoustics, Speech, and Signal Processing, 37 (10): 1568–1575, Oct. 1989.
[6] N. Fout, H. Akiba, K.-L. Ma, A. Lefohn, and J. Kniss, High-quality rendering of compressed volume data formats. In Proceedings of Eurographics-IEEE Symposium on Visualization, pages 77–84, 2005.
[7] M. Ghavamnia and X. Yang, Direct rendering of laplacian pyramid compressed volume data. In Visualization '95, pages 192–199, 1995.
[8] S. Guthe, M. Wand, J. Gonser, and W. Strasser, Interactive rendering of large volume data sets. In Visualization '02, pages 53–60, 2002.
[9] I. Ihm and S. Park, Wavelet-based 3d compression scheme for very large volume data. In Graphics Interface '98, pages 107–116, 1998.
[10] J. Kim and S. Lee, A transform domain classified vector quantizer for image coding. IEEE Transactions on Circuits and Systems for Video Technology, 2 (1): 3–14, Mar. 1992.
[11] T. Kim and Y. Shin, An efficient wavelet-based compression method for volume rendering. In Pacific Graphics '99, pages 147–156, 1999.
[12] M. Kraus and T. Ertl, Adaptive texture maps. In Proceedings of SIGGRAPH-Eurographics Workshop on Graphics Hardware, pages 7–15, 2002.
[13] A. Lefohn, J. Kniss, C. Hansen, and R. Whitaker, Interactive deformation and visualization of level set surfaces using graphics hardware. In Visualization '03, pages 75–82, 2003.
[14] Y. Linde, A. Buzo, and R. Gray, An algorithm for vector quantization design. IEEE Transactions on Communications, 28 (1): 84–95, Jan. 1980.
[15] E. Lum, K.-L. Ma, and J. Clyne, Texture hardware assisted rendering of time-varying volume data. In Visualization '01, pages 263–270, 2001.
[16] P. McCormick, J. Inman, J. Ahrens, C. Hansen, and G. Roth, Scout: A hardware-accelerated system for quantitatively driven visualization and analysis. In Visualization '04, pages 171–178, 2004.
[17] S. Muraki, Volume data and wavelet transforms. IEEE Computer Graphics and Applications, 13 (4): 50–56, 1993.
[18] K. Nguyen and D. Saupe, Rapid high quality compression of volume data for visualization. Computer Graphics Forum, 20 (3): 49–56, 2001.
[19] P. Ning and L. Hesselink, Vector quantization for volume rendering. In Symposium on Volume Visualization 1992, pages 69–74, 1992.
[20] P. Ning and L. Hesselink, Fast volume rendering of compressed data. In Visualization '93, pages 11–18, 1993.
[21] B. Ramamurthi and A. Gersho, Classified vector quantization of images. IEEE Transactions on Communications, 34 (11): 1105–1115, 1986.
[22] F. Rodler, Wavelet based 3d compression with fast random access for very large volume data. In Pacific Graphics '99, pages 108–117, 1999.
[23] J. Schneider and R. Westermann, Compression domain volume rendering. In Visualization '03, pages 293–300, 2003.
[24] Y. Shoham and A. Gersho, Efficient bit allocation for an arbitrary set of quantizers. IEEE Transactions on Acoustics, Speech, and Signal Processing, 36 (9): 1445–1453, 1988.
[25] B. Yeo and B. Liu, Volume rendering of dct-based compressed 3d scalar data. IEEE Transactions on Visualization and Computer Graphics, 1 (1): 29–43, 1995.

Index Terms:
Volume Compression, Compressed Volume Rendering, Transform Coding, Hardware-accelerated Volume Rendering
Citation:
Nathaniel Fout, Kwan-Liu Ma, "Transform Coding for Hardware-accelerated Volume Rendering," IEEE Transactions on Visualization and Computer Graphics, vol. 13, no. 6, pp. 1600-1607, Nov.-Dec. 2007, doi:10.1109/TVCG.2007.70606
Usage of this product signifies your acceptance of the Terms of Use.