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Streamlining context models for data compression

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2 Author(s)
Lelewer, D.A. ; Dept. of Inf. & Comput. Sci., California Univ., Irvine, CA, USA ; Hirschberg, D.S.

While context-modeling algorithms provide very good compression, they suffer from the disadvantages of being slow and requiring large amounts of main memory. A context-model-based algorithm is described that runs significantly faster, uses much less space, and provides compression ratios close to those of earlier context modeling algorithms. These improvements are achieved through use of self-organizing lists

Published in:

Data Compression Conference, 1991. DCC '91.

Date of Conference:

8-11 Apr 1991