Universal coding with different modelers in data compression
Reggie Kwan · Montana State University ScholarWorks (Montana State University) · 1987
In data compression systems, most existing codes have integer code length because of the nature of block codes.One of the ways to improve compression is to use codes with noninteger length.We developed an arithmetic universal coding scheme to achieve the entropy of an information source with the given probabilities from the modeler.We show how the universal coder can be used for both the probabilistic and nonprobabilistic approaches in data compression.to avoid a model file being too large, nonadaptive models are transformed into adaptive models simply by keeping the appropriate counts of symbols or strings.The idea of the universal coder is from the Elias code so it is quite close to the arithmetic code suggested by Rissanen and Langdon.The major difference is the way that we handle the precision problem and the carry-over problem.Ten to twenty percent extra compression can be expected as a result of using the universal coder.The process of adaptive modeling, however, may be forty times slower unless parallel algorithms are used to speed up the probability calculating process.