Abstract
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.
Original language | English |
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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 |
Keywords
- Image compression
- context modeling
- JBIG, parallel algorithms
- EREW
- PRAM