Header

UZH-Logo

Maintenance Infos

Multiresolution Volume Filtering in the Tensor Compressed Domain


Ballester-Ripoll, Rafael; Steiner, David; Pajarola, R (2018). Multiresolution Volume Filtering in the Tensor Compressed Domain. IEEE Transactions on Visualization and Computer Graphics, 24(10):2714-2727.

Abstract

Signal processing and filter operations are important tools for visual data processing and analysis. Due to GPU memory and bandwidth limitations, it is challenging to apply complex filter operators to large-scale volume data interactively. We propose a novel and fast multiscale compression-domain volume filtering approach integrated into an interactive multiresolution volume visualization framework. In our approach, the raw volume data is decomposed offline into a compact hierarchical multiresolution tensor approximation model. We then demonstrate how convolution filter operators can effectively be applied in the compressed tensor approximation domain. To prevent aliasing due to multiresolution filtering, our solution (a) filters accurately at the full spatial volume resolution at a very low cost in the compressed domain, and (b) reconstructs and displays the filtered result at variable level-of-detail. The proposed system is scalable, allowing interactive display and filtering of large volume datasets that may exceed the available GPU memory. The desired filter kernel mask and size can be modified online, producing immediate visual results.

Abstract

Signal processing and filter operations are important tools for visual data processing and analysis. Due to GPU memory and bandwidth limitations, it is challenging to apply complex filter operators to large-scale volume data interactively. We propose a novel and fast multiscale compression-domain volume filtering approach integrated into an interactive multiresolution volume visualization framework. In our approach, the raw volume data is decomposed offline into a compact hierarchical multiresolution tensor approximation model. We then demonstrate how convolution filter operators can effectively be applied in the compressed tensor approximation domain. To prevent aliasing due to multiresolution filtering, our solution (a) filters accurately at the full spatial volume resolution at a very low cost in the compressed domain, and (b) reconstructs and displays the filtered result at variable level-of-detail. The proposed system is scalable, allowing interactive display and filtering of large volume datasets that may exceed the available GPU memory. The desired filter kernel mask and size can be modified online, producing immediate visual results.

Statistics

Citations

Dimensions.ai Metrics
6 citations in Web of Science®
7 citations in Scopus®
Google Scholar™

Altmetrics

Downloads

2 downloads since deposited on 20 Feb 2019
0 downloads since 12 months
Detailed statistics

Additional indexing

Item Type:Journal Article, refereed, original work
Communities & Collections:03 Faculty of Economics > Department of Informatics
Dewey Decimal Classification:000 Computer science, knowledge & systems
Scopus Subject Areas:Physical Sciences > Software
Physical Sciences > Signal Processing
Physical Sciences > Computer Vision and Pattern Recognition
Physical Sciences > Computer Graphics and Computer-Aided Design
Uncontrolled Keywords:visualization, tensor approximation, multiresolution, volume rendering, compression-domain
Language:English
Date:October 2018
Deposited On:20 Feb 2019 15:44
Last Modified:29 Jul 2020 09:30
Publisher:Institute of Electrical and Electronics Engineers
ISSN:1077-2626
OA Status:Closed
Publisher DOI:https://doi.org/10.1109/TVCG.2017.2771282
Official URL:https://ieeexplore.ieee.org/document/8100972
Other Identification Number:merlin-id:17268

Download

Closed Access: Download allowed only for UZH members