Context-based compression of binary images in parallel

Eugene Ageenko (Corresponding Author), Martti Forsell, Pasi Fränti

    Research output: Contribution to journalArticleScientificpeer-review

    1 Citation (Scopus)


    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 languageEnglish
    Pages (from-to)1223-1237
    JournalSoftware: Practice and Experience
    Issue number13
    Publication statusPublished - 2002
    MoE publication typeA1 Journal article-refereed


    • Image compression
    • context modeling
    • JBIG, parallel algorithms
    • EREW
    • PRAM


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