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| Content Provider | ACM Digital Library |
|---|---|
| Author | Cai, Kangying Chen, QuQing Teng, Jun Wang, Wencheng Chen, Zhibo Jin, Yu |
| Abstract | We propose a new compression algorithm for massive models, which consist of a large number of small to medium sized connected components. It is by efficiently exploiting repetitive patterns in the input model. Compared with the similar work by finding repetitive patterns, our new algorithm is more efficient on detecting repeated components by recognizing instances repeating in various scalings. We also propose an efficient compression scheme for transformation data. As a result, it can achieve a considerably higher compression ratio. |
| Starting Page | 229 |
| Ending Page | 230 |
| Page Count | 2 |
| File Format | |
| ISBN | 9781605588698 |
| DOI | 10.1145/1643928.1643979 |
| Language | English |
| Publisher | Association for Computing Machinery (ACM) |
| Publisher Date | 2009-11-18 |
| Publisher Place | New York |
| Access Restriction | Subscribed |
| Subject Keyword | Compression Automatic discovery Repeated pattern |
| Content Type | Text |
| Resource Type | Article |
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