Real-time deblocked GPU rendering of compressed volumes
Fabio Marton, José Antonio Iglesias Guitián, Jose Diaz, and Enrico Gobbetti
October 2014
Abstract
The wide majority of current state-of-the-art compressed GPU volume renderers are based on block-transform coding, which is susceptible to blocking artifacts, particularly at low bit-rates. In this paper we address the problem for the first time, by introducing a specialized deferred filtering architecture working on block-compressed data and including a novel deblocking algorithm. The architecture efficiently performs high quality shading of massive datasets by closely coordinating visibility- and resolution-aware adaptive data loading with GPU-accelerated per-frame data decompression, deblocking, and rendering. A thorough evaluation including quantitative and qualitative measures demonstrates the performance of our approach on large static and dynamic datasets including a massive 512^4 turbulence simulation (256GB), which is aggressively compressed to less than 2 GB, so as to fully upload it on graphics board and to explore it in real-time during animation.
Reference and download information
Fabio Marton, José Antonio Iglesias Guitián, Jose Diaz, and Enrico Gobbetti. Real-time deblocked GPU rendering of compressed volumes. In Proc. 19th International Workshop on Vision, Modeling and Visualization (VMV). Pages 167-174, October 2014. DOI: 10.2312/vmv.20141290.
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Bibtex citation record
@InProceedings{Marton:2014:RDG, author = {Fabio Marton and {Jos\'e Antonio} {Iglesias Guiti\'an} and Jose Diaz and Enrico Gobbetti}, title = {Real-time deblocked GPU rendering of compressed volumes}, booktitle = {Proc. 19th International Workshop on Vision, Modeling and Visualization (VMV)}, pages = {167-174}, month = {October}, year = {2014}, abstract = { The wide majority of current state-of-the-art compressed GPU volume renderers are based on block-transform coding, which is susceptible to blocking artifacts, particularly at low bit-rates. In this paper we address the problem for the first time, by introducing a specialized deferred filtering architecture working on block-compressed data and including a novel deblocking algorithm. The architecture efficiently performs high quality shading of massive datasets by closely coordinating visibility- and resolution-aware adaptive data loading with GPU-accelerated per-frame data decompression, deblocking, and rendering. A thorough evaluation including quantitative and qualitative measures demonstrates the performance of our approach on large static and dynamic datasets including a massive $512^4$ turbulence simulation (256GB), which is aggressively compressed to less than $2$ GB, so as to fully upload it on graphics board and to explore it in real-time during animation. }, doi = {10.2312/vmv.20141290}, url = {http://vic.crs4.it/vic/cgi-bin/bib-page.cgi?id='Marton:2014:RDG'}, }
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