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Parallel Algorithms for Entropy-Coding Techniques



Abdou S. Youssef


With the explosion of imaging applications, and due to the massive amounts of imagery data, data compression is essential. Lossless compression also called entropy coding, is of special importance because not only it serves as a stand-alone system for certain applications such as medical imaging, it also is an inherent part of lossy compression. Therefore, fast entropy coding/decoding algorithms are desirable. In this paper we will develop parallel algorithms for several widely used entropy coding techniques, namely, arithmetic coding, run-length encoding (RLE), and Huffman coding. Our parallel arithmetic coding algorithm takes O(log2 N) time on an N-processor hypercube, where N is the input size. For RLE, our parallel coding and decoding algorithms take O(log N) time on N processors. Finally, in the case of Huffman coding, the parallel coding algorithm takes O(log2 N + n log n), where n is the alphabet size, n
- 6113
Report Number


arithmetic coding, decoding, Huffman coding, hypercube, parallet algorithms, run-length encoding, statistics gathering


Youssef, A. (1998), Parallel Algorithms for Entropy-Coding Techniques, - 6113, National Institute of Standards and Technology, Gaithersburg, MD (Accessed June 15, 2024)


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Created December 1, 1998, Updated October 16, 2008