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.
Beschreibung
Balanced Sampling and Compression for Remote Visualization
%0 Conference Paper
%1 frey2015balanced
%A Frey, Steffen
%A Sadlo, Filip
%A Ertl, Thomas
%B Proceedings of the SIGGRAPH Asia Symposium on High Performance Computing
%D 2015
%I ACM
%K 2015 A02 from:leonkokkoliadis sfbtrr161 visus visus:ertl visus:freysn
%P 1-4
%R 10.1145/2818517.2818529
%T Balanced Sampling and Compression for Remote Visualization
%U https://doi.org/10.1145/2818517.2818529
%X 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.
%@ 978-1-4503-3929-2
@inproceedings{frey2015balanced,
abstract = {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.},
added-at = {2020-10-09T12:31:49.000+0200},
author = {Frey, Steffen and Sadlo, Filip and Ertl, Thomas},
biburl = {https://puma.ub.uni-stuttgart.de/bibtex/2225c50ea9cfa2d2283b79152531370cc/mueller},
booktitle = {Proceedings of the SIGGRAPH Asia Symposium on High Performance Computing},
description = {Balanced Sampling and Compression for Remote Visualization},
doi = {10.1145/2818517.2818529},
interhash = {1f6c4520fe1d8b2756682e7ab96841e6},
intrahash = {225c50ea9cfa2d2283b79152531370cc},
isbn = {978-1-4503-3929-2},
keywords = {2015 A02 from:leonkokkoliadis sfbtrr161 visus visus:ertl visus:freysn},
pages = {1-4},
publisher = {ACM},
timestamp = {2020-10-09T10:31:49.000+0200},
title = {Balanced Sampling and Compression for Remote Visualization},
url = {https://doi.org/10.1145/2818517.2818529},
year = 2015
}
