Detail-preserving compression for smoke-based flow visualization


Smoke is a useful method to visualize flows from scientific experiments or physical simulations in many applications of science, engineering, graphics, and virtual reality. In visualizing flows, smoke evolution inside a flow field creates 3D, high-resolution, and time-varying data sets. The large data size imposes challenge on storing and transmitting the smoke animation results where good compression techniques are demanded. Furthermore, small-scale smoke details play important roles in visualizing and conveying realistic flow behavior. They should be well preserved in compression and reconstructed to create smooth animation effect in decompression. This requirement impairs the direct adaptation of existing techniques in video and volume compression. In this paper, we design new techniques to enable effective smoke visualization compression with smooth detail preservation. The motion estimation between key frames of density fields is implemented by a special bidirectional advection. The intermediate frames are built from advected key frames with specific blending. The advection is driven by motion vectors over nonuniform blocks, which are created with an adaptive simplification of velocity field to reflect the heterogeneous velocity variations. Moreover, pertinent intra-frame compression techniques are integrated. Our approach eventually achieves good compression performance and quality with easy control.

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  1. Agranovsky A, Camp D, Joy KI, Childs H (2015) Subsampling-based compression and flow visualization. Proc Vis Data Anal.

    Google Scholar 

  2. Balsa Rodriguez M, Gobbetti E, Iglesias Guitián J, Makhinya M, Marton F, Pajarola R, Suter S (2013) A survey of compressed GPU-based direct volume rendering. In: Eurographics state-of-the-art report

  3. Balsa Rodríguez M, Gobbetti E, Iglesias Guitián J, Makhinya M, Marton F, Pajarola R, Suter S (2014) State-of-the-art in compressed GPU-based direct volume rendering. Comput Graph Forum 33(6):77–100

    Article  Google Scholar 

  4. Beach A (2018) Video compression handbook, 2nd edn. Peachpit Press, Berkeley

    Google Scholar 

  5. Briceño HM, Sander PV, McMillan L, Gortler S, Hoppe H (2003) Geometry videos: a new representation for 3D animations. In: Proceedings of the 2003 ACM SIGGRAPH/Eurographics symposium on computer animation, SCA’03, Aire-la-Ville, Switzerland, Switzerland, Eurographics Association, pp 136–146

  6. Crane K, Llamas I, Tariq S (2007) Real time simulation and rendering of 3D fluids, chapter 30. Addison-Wesley, Boston

    Google Scholar 

  7. Doumanoglou A, Alexiadis DS, Zarpalas D, Daras P (2014) Toward real-time and efficient compression of human time-varying meshes. IEEE Trans Circuits Syst Video Technol 12(24):1051

    Google Scholar 

  8. Guthe S, Goesele M (2016) GPU-based lossless volume data compression. In: 3DTV-conference: the true vision—capture, transmission and display of 3D video

  9. Haller G (2001) Distinguished material surfaces and coherent structures in 3D fluid flows. Physica D 149:248–277

    MathSciNet  Article  MATH  Google Scholar 

  10. Heckel B, Weber G, Hamann B, Joy KI (1999) Construction of vector field hierarchies. In: Proceedings of the conference on visualization’99: celebrating ten years, VIS’99, Los Alamitos, CA, USA. IEEE Computer Society Press, pp 19–25

  11. Jang Y, Ebert D, Gaither K (2012) Time-varying data visualization using functional representations. IEEE Trans Vis Comput Graph 18(3):421–433

    Article  Google Scholar 

  12. Ko C-L, Liao H-S, Wang T-P, Fu K-W, Lin C-Y, Chuang J-H (2008) Multi-resolution volume rendering of large time-varying data using video-based compression. In: IEEE Pacific visualization. IEEE, pp 135–142

  13. Li S, Marsaglia N, Garth C, Woodring J, Clyne J, Childs H (2018) Data reduction techniques for simulation, visualization and data analysis. Comput Graph Forum 6(37):422–447

    Article  Google Scholar 

  14. Lodha SK, Faaland NM, Renteria JC (2003) Topology preserving top-down compression of 2D vector fields using bintree and triangular quadtrees. IEEE Trans Vis Comput Graph 9:433–442

    Article  Google Scholar 

  15. Maglo A, Lavoué G, Dupont F, Hudelot C (2015) 3D mesh compression: survey, comparisons, and emerging trends. ACM Comput Surv 47(3):44:1–44:41

    Article  Google Scholar 

  16. Mantiuk R, Efremov A, Myszkowski K, Seidel H-P (2006) Backward compatible high dynamic range mpeg video compression. In: ACM SIGGRAPH 2006 Papers, SIGGRAPH’06, New York, NY, USA. ACM, pp 713–723

  17. Mensmann J, Ropinski T, Hinrichs K (2010) A GPU-supported lossless compression scheme for rendering time-varying volume data. In: Proceedings of the 8th IEEE/EG international conference on volume graphics, VG’10, pp 109–116

  18. Peng J, Kim C-S, Jay Kuo C (2005) Technologies for 3D mesh compression: a survey. J Vis Commun Image Represent 16:688–733

    Article  Google Scholar 

  19. Salomon D (2006) Data compression: the complete reference. Springer, Berlin

    Google Scholar 

  20. Sattler M, Sarlette R, Klein R (2005) Simple and efficient compression of animation sequences. In: Proceedings of the 2005 ACM SIGGRAPH/Eurographics symposium on computer animation, SCA’05, New York, NY, USA. ACM, pp 209–217

  21. Selle A, Fedkiw R, Kim B, Liu Y, Rossignac J (2008) An unconditionally stable maccormack method. J Sci Comput 35:350–371

    MathSciNet  Article  MATH  Google Scholar 

  22. Skraba P, Wang B, Chen G, Rosen P (2014) 2D vector field simplification based on robustness. In: IEEE Pacific, pp 49–56

  23. Telea A, van Wijk JJ (1999) Simplified representation of vector fields. In: Proceedings of the conference on visualization’99: celebrating ten years, VIS’99, Los Alamitos, CA, USA. IEEE Computer Society Press, pp 35–42

  24. Theisel H, Rössl C, Seidel H-P (2003) Compression of 2D vector fields under guaranteed topology preservation. Comput Graph Forum 22(3):333–342

    Article  Google Scholar 

  25. Yu Ding Z, Gang Tan J, Yang Wu X, Feng Chen W, Ran Wu F, Li X, Chen W (2015) A near lossless compression domain volume rendering algorithm for floating-point time-varying volume data. J Vis 2(18):147–157

    Google Scholar 

  26. Yuan Z, Chen F, Zhao Y (2011) Pattern-guided smoke animation with Lagrangian coherent structure. ACM Trans Graph (SIGGRAPH Asia 2011) 30:136:1–136:8

    Google Scholar 

  27. Ziv J, Lempel A (2006) A universal algorithm for sequential data compression. IEEE Trans Inf Theor 23(3):337–343

    MathSciNet  Article  MATH  Google Scholar 

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Correspondence to Ye Zhao.

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Yuan, Z., Zhao, Y., Chen, F. et al. Detail-preserving compression for smoke-based flow visualization. J Vis 22, 51–64 (2019).

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  • Smoke compression
  • Flow visualization
  • Detail preserving