Interactive spatio-temporal exploration of massive time-Varying rectilinear scalar volumes based on a variable bit-rate sparse representation over learned dictionaries (original) (raw)

2020, Computers & Graphics

Abstract

sparkles

AI

We introduce a novel approach for supporting fully interactive non-linear spatio-temporal exploration of massive time-varying rectilinear scalar volumes on commodity platforms. The method utilizes a compressed representation based on a learned data-dependent dictionary, allowing it to efficiently handle large datasets while maintaining high-quality visualizations. By decomposing each frame into an octree of overlapping bricks and implementing adaptive streaming techniques, the approach ensures real-time decoding performance and supports bandwidth-constrained scenarios without introducing unwanted dynamic effects typically seen in traditional incremental loading methods.

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References (55)

  1. Weiss, K, Floriani, L. Modeling and visualization approaches for time- varying volumetric data. In: Proc. Advances in Visual Computing. 2008, p. 1000-1010.
  2. She, B, Boulanger, P, Noga, M. Real-time rendering of temporal volumetric data on a GPU. In: Proc. IEEE InfoVis. 2011, p. 622-631.
  3. Li, Y, Perlman, E, Wan, M, Yang, Y, Meneveau, C, Burns, R, et al. A public turbulence database cluster and applications to study Lagrangian evolution of velocity increments in turbulence. Journal of Turbulence 2008;9.
  4. Irion, R. The terascale supernova initiative: Modeling the first instance of a star's death. SciDAC Review 2006;2(1):26-37.
  5. Marton, F, Agus, M, Gobbetti, E. A framework for gpu-accelerated exploration of massive time-varying rectilinear scalar volumes. Computer Graphics Forum 2019;38(3).
  6. Díaz, J, Marton, F, Gobbetti, E. MTV-Player: Interactive spatio-temporal exploration of compressed large-scale time-varying rectilinar scalar vol- umes. In: Proc. STAG. 2019, p. 1-10.
  7. Balsa Rodriguez, M, Gobbetti, E, Iglesias Guitián, J, Makhinya, M, Marton, F, Pajarola, R, et al. State-of-the-art in compressed GPU-based direct volume rendering. Computer Graphics Forum 2014;33(6):77-100.
  8. Beyer, J, Hadwiger, M, Pfister, H. State-of-the-art in GPU-based large- scale volume visualization. Computer Graphics Forum 2015;34(8):13-37.
  9. Crassin, C, Neyret, F, Lefebvre, S, Eisemann, E. GigaVoxels: Ray- guided streaming for efficient and detailed voxel rendering. In: Proc. I3D. 2009, p. 15-22.
  10. Engel, K. CERA-TVR: A framework for interactive high-quality teravoxel volume visualization on standard PCs. In: Proc. IEEE LDAV. 2011, p. 123-124.
  11. Gobbetti, E, Iglesias Guitián, J, Marton, F. COVRA: A compression- domain output-sensitive volume rendering architecture based on a sparse representation of voxel blocks. Computer Graphics Forum 2012;31(3/4):1315-1324.
  12. Reichl, F, Treib, M, Westermann, R. Visualization of big SPH simulations via compressed octree grids. In: Proc. IEEE Big Data. 2013, p. 71-78.
  13. Treib, M, Burger, K, Reichl, F, Meneveau, C, Szalay, A, Westermann, R. Turbulence visualization at the terascale on desktop PCs. IEEE TVCG 2012;18(12):2169-2177.
  14. Hadwiger, M, Beyer, J, Jeong, WK, Pfister, H. Interactive volume exploration of petascale microscopy data streams using a visualization- driven virtual memory approach. IEEE TVCG 2012;18(12):2285-2294.
  15. Fogal, T, Schiewe, A, Kruger, J. An analysis of scalable GPU-based ray-guided volume rendering. In: Proc. IEEE LDAV. 2013, p. 43-51.
  16. Delp, E, Mitchell, O. Image compression using block truncation coding. IEEE Trans Comm 1979;27(9):1335-1342.
  17. Craighead, M. Gl nv texture compression vtc. OpenGL Extension Reg- istry; 2004.
  18. Yela, H, Navazo, I, Vazquez, P. S3Dc: A 3Dc-based volume compression algorithm. Computer Graphics 2008;:95-104.
  19. Iglesias Guitián, JA, Gobbetti, E, Marton, F. View-dependent exploration of massive volumetric models on large scale light field displays. The Visual Computer 2010;26(6-8):1037-1047.
  20. Fout, N, Ma, KL. Transform coding for hardware-accelerated volume rendering. IEEE TVCG 2007;13(6):1600-1607.
  21. Parys, R, Knittel, G. Giga-voxel rendering from compressed data on a display wall. In: Proc. WSCG. 2009, p. 73-80.
  22. Schneider, J, Westermann, R. Compression domain volume rendering. In: Proc. IEEE Vis. 2003, p. 293-300.
  23. Kraus, M, Ertl, T. Adaptive texture maps. In: Proc. Graphics Hardware. 2002, p. 7-15.
  24. Guthe, S, Goesele, M. Variable length coding for GPU-based direct volume rendering. In: Proc. VMV. 2016, p. 77-84.
  25. Yu, S, Zhang, S, Wang, K, Xia, Y, Zhang, H. An efficient and fast GPU- based algorithm for visualizing large volume of 4D data from virtual heart simulations. Biomedical Signal Processing and Control 2017;35:8-18.
  26. Aharon, M, Elad, M, Bruckstein, A. K-SVD: An algorithm for de- signing overcomplete dictionaries for sparse representation. IEEE TSP 2006;54(11):4311-4322.
  27. Lindstrom, . Fixed-rate compressed floating point arrays. IEEE TVCG 2014;20(12):2674-2683.
  28. Amorim, P, Franco de Moraes, T, Silva, J, Pedrini, H. Out-of-core rendering of large volumetric data sets at multiple levels of detail: Appli- cations and computational techniques. In: Multi-Modality Imaging. 2018, p. 191-215.
  29. Suter, S, Iglesias Guitián, J, Marton, F, Agus, M, Elsener, A, Zollikofer, C, et al. Interactive multiscale tensor reconstruction for multiresolution volume visualization. IEEE TVCG 2011;17(12):2135-2143.
  30. Ballester-Ripoll, R, Lindstrom, P, Pajarola, R. TTHRESH: Tensor com- pression for multidimensional visual data. arXiv preprint arXiv:180605952 2018;.
  31. Park, J, Gutenko, I, E. Kaufman, A. Transfer function-guided saliency- aware compression for transmitting volumetric data. IEEE Transactions on Multimedia 2017;PP:1-1.
  32. Shen, HW, Johnson, CR. Differential volume rendering: A fast volume visualization technique for flow animation. In: Proc. IEEE Vis. 1994, p. 180-187.
  33. Guthe, S, Straßer, W. Real-time decompression and visualization of animated volume data. In: Proc. IEEE Vis. IEEE; 2001, p. 349-572.
  34. Lum, EB, Ma, KL, Clyne, J. A hardware-assisted scalable solution for interactive volume rendering of time-varying data. IEEE TVCG 2002;8(3):286-301.
  35. Woodring, J, Wang, C, Shen, HW. High dimensional direct rendering of time-varying volumetric data. In: Proc. IEEE Vis. 2003, p. 417-424.
  36. Wang, H, Wu, Q, Shi, L, Yu, Y, Ahuja, N. Out-of-core tensor approximation of multi-dimensional matrices of visual data. ACM TOG 2005;24(3):527-535.
  37. Westermann, R. Compression domain rendering of time-resolved volume data. In: Proc.IEEE Vis. 1995, p. 168-175.
  38. Ma, KL, Shen, HW. Compression and accelerated rendering of time- varying volume data. In: Proc. International Workshop on Computer Graphics and Virtual Reality. 2000, p. 82-89.
  39. Wang, C, Gao, J, Li, L, Shen, HW. A multiresolution volume render- ing framework for large-scale time-varying data visualization. In: Proc. Volume Graphics. 2005, p. 11-19.
  40. Shen, HW. Visualization of large scale time-varying scientific data. Journal of Physics 2006;46(1):535-544.
  41. Ko, CL, Liao, HS, Wang, TP, Fu, KW, Lin, CY, Chuang, JH. Multi- resolution volume rendering of large time-varying data using video-based compression. In: Proc. IEEE Pacific Vis. 2008, p. 135-142.
  42. Mensmann, J, Ropinski, T, Hinrichs, K. A GPU-supported lossless compression scheme for rendering time-varying volume data. In: Proc. Volume Graphics. 2010, p. 109-116.
  43. Wang, C, Yu, H, Ma, KL. Importance-driven time-varying data visual- ization. IEEE TVCG 2008;14(6):1547-1554.
  44. Jang, Y, Ebert, DS, Gaither, KP. Time-varying data visualization using functional representations. IEEE TVCG 2012;18(3):421-433.
  45. Nagayasu, D, Ino, F, Hagihara, K. Two-stage compression for fast volume rendering of time-varying scalar data. In: Proc. GRAPHITE. 2008, p. 275-284.
  46. Wang, C, Yu, H, Ma, KL. Application-driven compression for visualizing large-scale time-varying data. IEEE CGA 2010;30(1):59-69.
  47. Cao, Y, Wu, G, Wang, H. A smart compression scheme for GPU- accelerated volume rendering of time-varying data. In: Proc. IEEE ICVRV. 2011, p. 205-210.
  48. Pulido, J, Livescu, D, Kanov, K, Burns, RC, Canada, C, Ahrens, JP, et al. Remote visual analysis of large turbulence databases at multiple scales. J Parallel Distrib Comput 2018;120:115-126.
  49. Rubinstein, R, Zibulevsky, M, Elad, M. Efficient implementation of the K-SVD algorithm using batch orthogonal matching pursuit. Tech. Rep.; CS Technion; 2008.
  50. Efraimidis, PS. Weighted random sampling over data streams. In: Algo- rithms, Probability, Networks, and Games. 2015, p. 183-195.
  51. JHU, . Johns Hopkins Turbulence Databases. http://turbulence. pha.jhu.edu/datasets.aspx; 2016. [accessed: 2018-10-31].
  52. Nystad, J, Lassen, A, Pomianowski, A, Ellis, S, Olson, T. Adaptive scalable texture compression. In: Proc. HPG. 2012, p. 105-114.
  53. Di, S, Cappello, F. Fast error-bounded lossy HPC data compression with SZ. In: Proc. IEEE IPDPS. 2016, p. 730-739.
  54. Wang, Z, Bovik, A, Sheikh, H, Simoncelli, E. Image quality assessment: from error visibility to structural similarity. IEEE TIP 2004;13(4):600 -612.
  55. Marton, F, Iglesias Guitián, J, Diaz, J, Gobbetti, E. Real-time deblocked GPU rendering of compressed volumes. In: Proc. VMV. 2014, p. 167-174.