PPM Model Cleaning

The Prediction by Partial Matching (PPM) algorithm uses a cumulative frequency count of input symbols in different contexts to estimate their probability distribution. Excellent compression ratios yielded by the PPM algorithm have not instigated broader use of this scheme mainly because of its high demand for computational resources. In this paper, we present an algorithm which improves the memory usage by the PPM model. The algorithm heuristically identifies and removes portions of the PPM model which are not contributing toward better modeling of the input data. As a result, our algorithm improves the average compression ratio up to 7% under the memory limitation constraint at the expense of increased computation. Under the constraint of maintaining the same level of compression ratios, our algorithm reduces the memory usage up to 70%.

• Type: Conference
• Year: 2003
• Where: DCC
• Authors: Milenko Drinic, Darko Kirovski, Miodrag Potkonjak
• Keywords: Computer Graphics, DCC 2003, Memory Limitation Constraint, PPM Algorithm, PPM Model