We present a novel approach for handling sampling and compression in remote visualization in an integrative fashion.
As adaptive sampling and compression share the same underlying concepts and criteria, the times spent for visualization and transfer can be balanced directly to optimize the image quality that can be achieved within a prescribed time window. Our dynamic adjustments regarding adaptive sampling, compression, and balancing, employ regression analysis-based error estimation which is carried out individually for each image block of a visualization frame. Our approach is tuned for high parallel efficiency in GPU-based remote visualization. We demonstrate its utility within a prototypical remote volume visualization pipeline by means of different datasets and configurations.

Presenter(s)

Steffen Frey , University of Stuttgart

Filip Sadlo , IWR, University of Heidelberg

Thomas Ertl , University of Stuttgart