A Conditional Incremental-Runlength Code Based on Two-Dimensional Markov Model

Abstract

The conditional runlength code is known to be optimum for the Markov source model, but it is complicated in implementation and not efficient for practical line-based transmission. To avoid these disadvantages, a conditional incremental-runlength coding algorithm based on the same model is investigated. The entropy of the incremental runs is shown to be less than that of the runs, but additional information is required for the state identification. Yet, for a practical case with two composite states, the required state information is reduced so much that the new coding is shown to be better than the conditional runlength coding in line-based transmission. The coding efficiency is compared with some other well-known codes for the standard CCITT test documents. It is found to be one of the best codes, especially for the documents of high complexity.