Details
| Original language | English |
|---|---|
| Title of host publication | SCIA 2005 |
| Subtitle of host publication | Image Analysis |
| Pages | 302-311 |
| Number of pages | 10 |
| ISBN (electronic) | 978-3-540-31566-7 |
| Publication status | Published - 2005 |
| Event | 14th Scandinavian Conference on Image Analysis, SCIA 2005 - Joensuu, Finland Duration: 19 Jun 2005 → 22 Jun 2005 |
Publication series
| Name | Lecture Notes in Computer Science |
|---|---|
| Publisher | Springer Verlag |
| Volume | 3540 |
| ISSN (Print) | 0302-9743 |
Abstract
The aim is to compress and decompress structured volume graphics in a lossless way. Lossless compression is necessary when the original scans must be preserved. Algorithms must deliver a fair compression ratio, have low run-time and space complexity, and work numerically robust. We have developed PRO to meet the goals. PRO traces runs of voxels in 3D and compensates for noise in the least significant bits by way of using differential pulse-code modulation (DPCM). PRO reduces data to 46% of the original size at best, and 54% on average. A combination of PRO and Worst-Zip (Zip with weakest compression enabled) gives reductions of 34% at best, and 45% on average. The combination takes the same or less time than Best-Zip, and gives 13%, respectively 5%, better results. To conduct the tests, we have written a non-optimised, sequential prototype of PRO, processed CT and MRI scans of different size and content, and measured speed and compression ratio.
ASJC Scopus subject areas
- Mathematics(all)
- Theoretical Computer Science
- Computer Science(all)
- General Computer Science
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SCIA 2005: Image Analysis. 2005. p. 302-311 (Lecture Notes in Computer Science; Vol. 3540).
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review
}
TY - GEN
T1 - Efficient 1-pass prediction for volume compression
AU - Jensen, Nils
AU - Von Voigt, Gabriele
AU - Nejdl, Wolfgang
AU - Bernarding, Johannes
PY - 2005
Y1 - 2005
N2 - The aim is to compress and decompress structured volume graphics in a lossless way. Lossless compression is necessary when the original scans must be preserved. Algorithms must deliver a fair compression ratio, have low run-time and space complexity, and work numerically robust. We have developed PRO to meet the goals. PRO traces runs of voxels in 3D and compensates for noise in the least significant bits by way of using differential pulse-code modulation (DPCM). PRO reduces data to 46% of the original size at best, and 54% on average. A combination of PRO and Worst-Zip (Zip with weakest compression enabled) gives reductions of 34% at best, and 45% on average. The combination takes the same or less time than Best-Zip, and gives 13%, respectively 5%, better results. To conduct the tests, we have written a non-optimised, sequential prototype of PRO, processed CT and MRI scans of different size and content, and measured speed and compression ratio.
AB - The aim is to compress and decompress structured volume graphics in a lossless way. Lossless compression is necessary when the original scans must be preserved. Algorithms must deliver a fair compression ratio, have low run-time and space complexity, and work numerically robust. We have developed PRO to meet the goals. PRO traces runs of voxels in 3D and compensates for noise in the least significant bits by way of using differential pulse-code modulation (DPCM). PRO reduces data to 46% of the original size at best, and 54% on average. A combination of PRO and Worst-Zip (Zip with weakest compression enabled) gives reductions of 34% at best, and 45% on average. The combination takes the same or less time than Best-Zip, and gives 13%, respectively 5%, better results. To conduct the tests, we have written a non-optimised, sequential prototype of PRO, processed CT and MRI scans of different size and content, and measured speed and compression ratio.
UR - http://www.scopus.com/inward/record.url?scp=26444486894&partnerID=8YFLogxK
U2 - 10.1007/11499145_32
DO - 10.1007/11499145_32
M3 - Conference contribution
AN - SCOPUS:26444486894
SN - 978-3-540-26320-3
T3 - Lecture Notes in Computer Science
SP - 302
EP - 311
BT - SCIA 2005
T2 - 14th Scandinavian Conference on Image Analysis, SCIA 2005
Y2 - 19 June 2005 through 22 June 2005
ER -