Predictive encoding in text compression

Abstract In predictive text compression the characters are encoded one by one on the basis of a few preceding characters. The usage of contextual knowledge makes the compression more effective than the plain coding of characters independently of their neighbors. In the simplest case we merely try to guess the next character, and the success/ failure is encoded. Generally, the preceding substring determines the probability distribution of the successor, providing a basis for encoding. In this article, three compression methods of increasing power are presented. Special attention is paid to the trade-off between compression gain and processing time. As for speed, hashing turns out to be an ideal technique for maintaining the prediction information. The best gain is achieved by applying the optimal arithmetic coding to the successor information, extracted from the dependencies between characters.