1 Context-based Compression of Binary Images in Parallel
Abstract:
Binary images can be compressed efficiently using context-based statistical modeling and arithmetic coding. The problem of this kind of approach is that the process is fully sequential and therefore, additional computing power from parallel computers cannot be utilized. We attack this problem and show how the context-based compression can be implemented 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 how the construct, distribute and utilize the model in parallel, and study the effect on the compression performance and run time. We will show by experiments that the proposed approach achieves speedup that is proportional to the number of processors. The work efficiency of the extra processing exceeds 50 % with any reasonable number of processors. Keywords: image compression, context modeling, JBIG, parallel algorithms, EREW, PRAM.
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