Toward Feature-Preserving Vector Field Compression

The objective of this work is to develop error-bounded lossy compression methods to preserve topological features in 2D and 3D vector fields. Specifically, we explore the preservation of critical points in piecewise linear and bilinear vector fields. We define the preservation of critical points as,...

Ausführliche Beschreibung

Bibliographische Detailangaben
Veröffentlicht in:IEEE transactions on visualization and computer graphics. - 1996. - 29(2023), 12 vom: 01. Dez., Seite 5434-5450
1. Verfasser: Liang, Xin (VerfasserIn)
Weitere Verfasser: Di, Sheng, Cappello, Franck, Raj, Mukund, Liu, Chunhui, Ono, Kenji, Chen, Zizhong, Peterka, Tom, Guo, Hanqi
Format: Online-Aufsatz
Sprache:English
Veröffentlicht: 2023
Zugriff auf das übergeordnete Werk:IEEE transactions on visualization and computer graphics
Schlagworte:Journal Article
LEADER 01000caa a22002652c 4500
001 NLM34766802X
003 DE-627
005 20250303234428.0
007 cr uuu---uuuuu
008 231226s2023 xx |||||o 00| ||eng c
024 7 |a 10.1109/TVCG.2022.3214821  |2 doi 
028 5 2 |a pubmed25n1158.xml 
035 |a (DE-627)NLM34766802X 
035 |a (NLM)36251895 
040 |a DE-627  |b ger  |c DE-627  |e rakwb 
041 |a eng 
100 1 |a Liang, Xin  |e verfasserin  |4 aut 
245 1 0 |a Toward Feature-Preserving Vector Field Compression 
264 1 |c 2023 
336 |a Text  |b txt  |2 rdacontent 
337 |a ƒaComputermedien  |b c  |2 rdamedia 
338 |a ƒa Online-Ressource  |b cr  |2 rdacarrier 
500 |a Date Revised 10.11.2023 
500 |a published: Print-Electronic 
500 |a Citation Status PubMed-not-MEDLINE 
520 |a The objective of this work is to develop error-bounded lossy compression methods to preserve topological features in 2D and 3D vector fields. Specifically, we explore the preservation of critical points in piecewise linear and bilinear vector fields. We define the preservation of critical points as, without any false positive, false negative, or false type in the decompressed data, (1) keeping each critical point in its original cell and (2) retaining the type of each critical point (e.g., saddle and attracting node). The key to our method is to adapt a vertex-wise error bound for each grid point and to compress input data together with the error bound field using a modified lossy compressor. Our compression algorithm can be also embarrassingly parallelized for large data handling and in situ processing. We benchmark our method by comparing it with existing lossy compressors in terms of false positive/negative/type rates, compression ratio, and various vector field visualizations with several scientific applications 
650 4 |a Journal Article 
700 1 |a Di, Sheng  |e verfasserin  |4 aut 
700 1 |a Cappello, Franck  |e verfasserin  |4 aut 
700 1 |a Raj, Mukund  |e verfasserin  |4 aut 
700 1 |a Liu, Chunhui  |e verfasserin  |4 aut 
700 1 |a Ono, Kenji  |e verfasserin  |4 aut 
700 1 |a Chen, Zizhong  |e verfasserin  |4 aut 
700 1 |a Peterka, Tom  |e verfasserin  |4 aut 
700 1 |a Guo, Hanqi  |e verfasserin  |4 aut 
773 0 8 |i Enthalten in  |t IEEE transactions on visualization and computer graphics  |d 1996  |g 29(2023), 12 vom: 01. Dez., Seite 5434-5450  |w (DE-627)NLM098269445  |x 1941-0506  |7 nnas 
773 1 8 |g volume:29  |g year:2023  |g number:12  |g day:01  |g month:12  |g pages:5434-5450 
856 4 0 |u http://dx.doi.org/10.1109/TVCG.2022.3214821  |3 Volltext 
912 |a GBV_USEFLAG_A 
912 |a SYSFLAG_A 
912 |a GBV_NLM 
912 |a GBV_ILN_350 
951 |a AR 
952 |d 29  |j 2023  |e 12  |b 01  |c 12  |h 5434-5450