The Visual Computer

, Volume 32, Issue 3, pp 323–334 | Cite as

Interactive multilevel focus+context visualization framework

  • Mahmudul HasanEmail author
  • Faramarz F. Samavati
  • Christian Jacob
Original Article


In this article, we present the construction of an interactive multilevel focus+context visualization framework for the navigation and exploration of large-scale 2D and 3D images. The presented framework utilizes a balanced multiresolution technique supported by a balanced wavelet transform (BWT). It extends the mode of focus+context visualization, where spatially separate magnification of regions of interest (ROIs) is performed, as opposed to in-place magnification. Each resulting visualization scenario resembles a tree structure, where the root constitutes the main context, each non-root internal node plays the dual roles of both focus and context, and each leaf solely represents a focus. Our developed prototype supports interactive manipulation of the visualization hierarchy, such as addition and deletion of ROIs and desired changes in their resolutions at any level of the hierarchy on the fly. We describe the underlying data structure efficiently support such operations. Changes in the spatial locations of query windows defining the ROIs trigger on-demand reconstruction queries. We explain in detail how to efficiently process such reconstruction queries within the hierarchy of details (wavelet coefficients) contained in the BWT in order to ensure real-time feedback. As the BWT need only be constructed once in a preprocessing phase on the server-side and robust on-demand reconstruction queries require minimal data communication overhead, our presented framework is a suitable candidate for efficient web-based visualization of complex large-scale imagery. We also discuss the performance characteristics of our proposed framework from various aspects, such as time and space complexities and achieved frame rates.


Focus+context visualization Contextual close-up Multilevel hierarchy Balanced decomposition Perfect reconstruction Balanced wavelet transform 



This research received generous support from the Natural Sciences and Engineering Research Council (NSERC) of Canada, Alberta Innovates Technology Futures (AITF), Alberta Enterprise and Advanced Education, and Network of Centres of Excellence (NCE) of Canada in Graphics, Animation and New Media (GRAND). We would like to thank Mario Costa Sousa for his insightful discussions and Troy Alderson for his helpful editorial comments.

Supplementary material

Supplementary material 1 (mp4 62302 KB)


  1. 1.
    Bartels, R., Samavati, F.: Multiresolutions numerically from subdivisions. Comput. Graph. 35(2), 185–197 (2011). doi: 10.1016/j.cag.2010.12.001 CrossRefGoogle Scholar
  2. 2.
    Bartels, R.H., Golub, G.H., Samavati, F.F.: Some observations on local least squares. BIT Numer. Math. 46(3), 455–477 (2006). doi: 10.1007/s10543-006-0075-y MathSciNetCrossRefzbMATHGoogle Scholar
  3. 3.
    Card, S.K., Nation, D.: Degree-of-interest trees: a component of an attention-reactive user interface. In: Proceedings of the Working Conference on Advanced Visual Interfaces, AVI ’02, pp. 231–245. ACM, New York (2002). doi: 10.1145/1556262.1556300
  4. 4.
    Chaikin, G.M.: An algorithm for high-speed curve generation. Comput. Graph. Image Process. 3(4), 346–349 (1974). doi: 10.1016/0146-664X(74)90028-8 CrossRefGoogle Scholar
  5. 5.
    Cohen, M., Brodlie, K.: Focus and context for volume visualization. In: Proceeding of the Theory and Practice of Computer Graphics Conference, pp. 32–39. IEEE (2004). doi: 10.1109/TPCG.2004.1314450
  6. 6.
    Cossalter, M., Mengshoel, O.J., Selker, T.: Multi-focus and multi-level techniques for visualization and analysis of networks with thematic data. In: Proceeding of the SPIE Conference on Visualization and Data Analysis, vol. 8654, pp. 1–15 (2013). doi: 10.1117/12.2005096.865403
  7. 7.
    Hasan, M., Samavati, F.F., Jacob, C.: Multilevel focus+context visualization using balanced multiresolution. In: Proceedings of the International Conference on Cyberworlds, CW, pp. 145–152. IEEE Computer Society (2014). doi: 10.1109/CW.2014.28
  8. 8.
    Hasan, M., Samavati, F.F., Sousa, M.C.: Balanced multiresolution for symmetric/antisymmetric filters. Graphi. Models 78, 36–59 (2015). doi: 10.1016/J.GMOD.2015.01.001 CrossRefGoogle Scholar
  9. 9.
    Hauser, H.: Generalizing focus+context visualization. In: Bonneau, G.P., Ertl, T., Nielson, G. (eds.) Scientific Visualization: The Visual Extraction of Knowledge from Data, Mathematics and Visualization, pp. 305–327. Springer, Berlin (2006). doi: 10.1007/3-540-30790-7_18
  10. 10.
    Hodges, E.R.S.: The Guild Handbook of Scientific Illustration. Wiley, Hoboken (2003)Google Scholar
  11. 11.
    Hsu, W.H., Ma, K.L., Correa, C.: A rendering framework for multiscale views of 3D models. In: Proceedings of the SIGGRAPH Asia Conference, SA, pp. 131:1–131:10. ACM, New York (2011). doi: 10.1145/2024156.2024165
  12. 12.
    Kalkofen, D., Mendez, E., Schmalstieg, D.: Interactive focus and context visualization for augmented reality. In: IEEE/ACM International Symposium on Mixed and Augmented Reality, ISMAR, pp. 191–201. IEEE (2007). doi: 10.1109/ISMAR.2007.4538846
  13. 13.
    LaMar, E., Hamann, B., Joy, K.I.: Multiresolution techniques for interactive texture-based volume visualization. In: Proceedings of the Conference on Visualization. VIS, pp. 355–361. IEEE Computer Society Press, Los Alamitos (1999)Google Scholar
  14. 14.
    Losasso, F., Hoppe, H.: Geometry clipmaps: terrain rendering using nested regular grids. In: ACM SIGGRAPH Papers, SIGGRAPH, pp. 769–776. ACM, New York (2004). doi: 10.1145/1186562.1015799
  15. 15.
    Mendez, E., Kalkofen, D., Schmalstieg, D.: Interactive context-driven visualization tools for augmented reality. In: IEEE/ACM International Symposium on Mixed and Augmented Reality, ISMAR, pp. 209–218. IEEE (2006). doi: 10.1109/ISMAR.2006.297816
  16. 16.
    Packer, J.F.: Focus+context via snaking paths. Master’s thesis, Department of Computer Science, University of Calgary, Calgary (2013).
  17. 17.
    Plate, J., Tirtasana, M., Carmona, R., Fröhlich, B.: Octreemizer: A hierarchical approach for interactive roaming through very large volumes. In: Proceedings of the Symposium on Data Visualisation, VISSYM, pp. 53–60. Eurographics Association, Aire-la-Ville, Switzerland (2002). doi: 10.2312/VisSym/VisSym02/053-060
  18. 18.
    Ropinski, T., Viola, I., Biermann, M., Hauser, H., Hinrichs, K.: Multimodal visualization with interactive closeups. In: Proceeding of the Theory and Practice of Computer Graphics Conference, pp. 17–24. Eurographics Association (2009). doi: 10.2312/LocalChapterEvents/TPCG/TPCG09/017-024
  19. 19.
    Samavati, F.F., Bartels, R.H.: Multiresolution curve and surface representation: reversing subdivision rules by least-squares data fitting. Comput. Graph. Forum 18(2), 97–119 (1999). doi: 10.1111/1467-8659.00361 CrossRefGoogle Scholar
  20. 20.
    Samavati, F.F., Bartels, R.H., Olsen, L.: Local B-spline multiresolution with examples in iris synthesis and volumetric rendering. In: Yanushkevich, S.N., Gavrilova, M.L., Wan, P.S.P., Srihari, S.N. (eds.) Image Pattern Recognition: Synthesis and Analysis in Biometrics, Machine Perception and Artificial Intelligence, vol. 67, pp. 65–102. World Scientific Publishing, Singapore (2007). doi: 10.1142/9789812770677_0003
  21. 21.
    Suter, S., Guitian, J.I., Marton, F., Agus, M., Elsener, A., Zollikofer, C., Gopi, M., Gobbetti, E., Pajarola, R.: Interactive multiscale tensor reconstruction for multiresolution volume visualization. IEEE Trans. Vis. Compu. Graph. 17(12), 2135–2143 (2011). doi: 10.1109/TVCG.2011.214 CrossRefGoogle Scholar
  22. 22.
    Taerum, T., Sousa, M.C., Samavati, F.F., Chan, S., Mitchell, J.R.: Real-time super resolution contextual close-up of clinical volumetric data. In: Proceedings of the Joint Eurographics—IEEE VGTC Symposium on Visualization, EuroVis, pp. 347–354. Eurographics Association (2006). doi: 10.2312/VisSym/EuroVis06/347-354
  23. 23.
    Tu, Y., Shen, H.W.: Balloon focus: a seamless multi-focus+context method for treemaps. IEEE Trans. Vis. Comput. Graph. 14(6), 1157–1164 (2008). doi: 10.1109/TVCG.2008.114 CrossRefGoogle Scholar
  24. 24.
    Wang, C., Shen, H.W.: Hierarchical navigation interface: leveraging multiple coordinated views for level-of-detail multiresolution volume rendering of large scientific data sets. In: Proceedings of the International Conference on Information Visualisation, pp. 259–267. IEEE (2005). doi: 10.1109/IV.2005.57
  25. 25.
    Wang, Y.S., Wang, C., Lee, T.Y., Ma, K.L.: Feature-preserving volume data reduction and focus+context visualization. IEEE Trans. Vis. Comput. Graph. 17(2), 171–181 (2011). doi: 10.1109/TVCG.2010.34 CrossRefGoogle Scholar
  26. 26.
    Wong, P.C., Shen, H.W., Johnson, C., Chen, C., Ross, R.B.: The top 10 challenges in extreme-scale visual analytics. IEEE Comput. Graph. Appl. 32(4), 63–67 (2012). doi: 10.1109/MCG.2012.87 CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2015

Authors and Affiliations

  • Mahmudul Hasan
    • 1
    Email author
  • Faramarz F. Samavati
    • 1
  • Christian Jacob
    • 1
    • 2
  1. 1.Department of Computer ScienceUniversity of CalgaryCalgaryCanada
  2. 2.Department of Biochemistry and Molecular BiologyUniversity of CalgaryCalgaryCanada

Personalised recommendations