Interactive, Internet Delivery of Visualization via Structured Prerendered Multiresolution Imagery

We present a novel approach for latency-tolerant delivery of visualization and rendering results, where client-side frame rate display performance is independent of source data set size, image size, visualization technique, or rendering complexity. Our approach delivers prerendered multiresolution images to a remote user as they navigate through different viewpoints, visualization parameters, or rendering parameters. We employ demand-driven tiled multiresolution image streaming and prefetching to efficiently utilize available bandwidth while providing the maximum resolution a user can perceive from a given viewpoint. Since image data is the only input to our system, our approach is generally applicable to all visualization and graphics rendering applications capable of generating v in an ordered fashion. In our implementation, a normal Web server provides on-demand images to a remote custom client application, which uses client-pull to obtain and cache only those images required to fulfill the interaction needs. The main contributions of this work are 1) an architecture for latency-tolerant remote delivery of precomputed imagery suitable for use with any visualization or rendering application capable of producing images in an ordered fashion; and 2) a performance study showing the impact of diverse network environments and different tunable system parameters on end-to-end system performance in terms of deliverable frames per second.

[1]  Dieter Kranzlmüller,et al.  Optimizations in the grid visualization kernel , 2002, Proceedings 16th International Parallel and Distributed Processing Symposium.

[2]  R. Daniel Bergeron,et al.  Visualization reference models , 1993, VIS '93.

[3]  Hans-Christian Hege,et al.  Interactive exploration of large remote micro-CT scans , 2004, IEEE Visualization 2004.

[4]  Brian D. Noble,et al.  Improving throughput and maintaining fairness using parallel TCP , 2004, IEEE INFOCOM 2004.

[5]  Kiril Vidimce,et al.  Lpics: a hybrid hardware-accelerated relighting engine for computer cinematography , 2005, SIGGRAPH 2005.

[6]  Ulrich Neumann,et al.  IBRAC (Image-Based Rendering Acceleration and Compression) , 2000 .

[7]  Daniel Cohen-Or,et al.  Visiblity Streaming for Network-based Walkthroughs , 1998, Graphics Interface.

[8]  Marc Levoy.  Polygon-assisted JPEG and MPEG compression of synthetic images , 1995, SIGGRAPH.

[9]  Aaas News,et al.  Book Reviews , 1893, Buffalo Medical and Surgical Journal.

[10]  Ulrich Neumann,et al.  Web‐Based Remote Renderingwith IBRAC (Image‐Based Rendering Acceleration and Compression) , 2000, Comput. Graph. Forum.

[11]  Andy Hopper,et al.  Virtual Network Computing , 1998, IEEE Internet Comput..

[12]  Jason Lee,et al.  Using High-Speed WANs and Network Data Caches to Enable Remote and Distributed Visualization , 2000, ACM/IEEE SC 2000 Conference (SC'00).

[13]  Ilmi Yoon,et al.  Interactive Internet delivery of scientific visualization via structured prerendered imagery , 2006, Electronic Imaging.

[14]  Shenchang Eric Chen,et al.  QuickTime VR: an image-based approach to virtual environment navigation , 1995, SIGGRAPH.

[15]  William E. Allcock,et al.  The Globus Striped GridFTP Framework and Server , 2005, ACM/IEEE SC 2005 Conference (SC'05).

[16]  Terry Moore,et al.  An end-to-end approach to globally scalable network storage , 2002, SIGCOMM 2002.

[17]  D. A. Duce,et al.  Visualization in Scientific Computing , 1994, Focus on Computer Graphics.

[18]  David Ellsworth,et al.  Concurrent Visualization in a Production Supercomputing Environment , 2006, IEEE Transactions on Visualization and Computer Graphics.

[19]  Douglas C. Schmidt,et al.  Transport System Architecture Services for High-Performance Communications Systems , 1993, IEEE J. Sel. Areas Commun..

[20]  John Shalf,et al.  Performance Modeling for 3D Visualization in a Heterogeneous Computing Environment , 2004 .

[21]  Brygg Ullmer,et al.  Progressive Retrieval and Hierarchical Visualization of Large Remote Data , 2001, Scalable Comput. Pract. Exp..

[22]  Micah Beck,et al.  An end-to-end approach to globally scalable network storage , 2002, SIGCOMM '02.

[23]  Hans-Christian Hege,et al.  Distributed Visualization with OpenGL Vizserver: Practical Experiences , 2000 .

[24]  Paul Heinzlreiter,et al.  Visualization Services on the Grid: The Grid Visualization Kernel , 2003, Parallel Process. Lett..

[25]  Kwan-Liu Ma,et al.  End-to-end data reduction and hardware accelerated rendering techniques for visualizing time-varying non-uniform grid volume data , 2005, Fourth International Workshop on Volume Graphics, 2005..

[26]  Charles D. Hansen,et al.  Semotus Visum: a flexible remote visualization framework , 2002, IEEE Visualization, 2002. VIS 2002..

[27]  John Shalf,et al.  Performance Modeling for Grid-Based Visualization , 2004 .

[28]  Bernd Hamann,et al.  NERSC “Visualization Greenbook” Future Visualization Needs of the Doe Computational Science Community Hosted at NERSC , 2003, Int. J. High Perform. Comput. Appl..

[29]  V. Pascucci,et al.  Global Static Indexing for Real-Time Exploration of Very Large Regular Grids , 2001, ACM/IEEE SC 2001 Conference (SC'01).

[30]  Thomas Ertl,et al.  Remote 3D Visualization using Image-Streaming Techniques , 2000 .

[31]  Kwan-Liu Ma,et al.  A Parallel Visualization Pipeline for Terascale Earthquake Simulations , 2004, Proceedings of the ACM/IEEE SC2004 Conference.

[32]  John Rohlf,et al.  IRIS performer: a high performance multiprocessing toolkit for real-time 3D graphics , 1994, SIGGRAPH.

[33]  Van Jacobson,et al.  TCP Extensions for High Performance , 1992, RFC.