Abstract
| Original language | English |
|---|---|
| Pages (from-to) | 1223-1237 |
| Journal | Software: Practice and Experience |
| Volume | 32 |
| Issue number | 13 |
| DOIs | |
| Publication status | Published - 2002 |
| MoE publication type | A1 Journal article-refereed |
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Keywords
- Image compression
- context modeling
- JBIG, parallel algorithms
- EREW
- PRAM
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Context-based compression of binary images in parallel. / Ageenko, Eugene (Corresponding Author); Forsell, Martti; Fränti, Pasi.
In: Software: Practice and Experience, Vol. 32, No. 13, 2002, p. 1223-1237.Research output: Contribution to journal › Article › Scientific › peer-review
TY - JOUR
T1 - Context-based compression of binary images in parallel
AU - Ageenko, Eugene
AU - Forsell, Martti
AU - Fränti, Pasi
PY - 2002
Y1 - 2002
N2 - Binary images can be compressed efficiently using context-based statistical modeling and arithmetic coding. However, this approach is fully sequential and therefore additional computing power from parallel computers cannot be utilized. We attack this problem and show how to implement the context-based compression in parallel. Our approach is to segment the image into non-overlapping blocks, which are compressed independently by the processors. We give two alternative solutions about how to construct, distribute and utilize the model in parallel, and study the effect on the compression performance and execution time. We show by experiments that the proposed approach achieves speedup that is proportional to the number of processors. The work efficiency exceeds 50% with any reasonable number of processors.
AB - Binary images can be compressed efficiently using context-based statistical modeling and arithmetic coding. However, this approach is fully sequential and therefore additional computing power from parallel computers cannot be utilized. We attack this problem and show how to implement the context-based compression in parallel. Our approach is to segment the image into non-overlapping blocks, which are compressed independently by the processors. We give two alternative solutions about how to construct, distribute and utilize the model in parallel, and study the effect on the compression performance and execution time. We show by experiments that the proposed approach achieves speedup that is proportional to the number of processors. The work efficiency exceeds 50% with any reasonable number of processors.
KW - Image compression
KW - context modeling
KW - JBIG, parallel algorithms
KW - EREW
KW - PRAM
U2 - 10.1002/spe.480
DO - 10.1002/spe.480
M3 - Article
VL - 32
SP - 1223
EP - 1237
JO - Software: Practice and Experience
JF - Software: Practice and Experience
SN - 0038-0644
IS - 13
ER -