We developed a simple, practical, adaptive data compression algorithm of the LZ78 class. According to the Lempel-Ziv greedy parsing, a string boundary is not related to the statistical history modeled by finite-state sources. We have already reported an algorithm classifying data into subdictionaries (CSD), which uses multiple subdictionaries and conditions the current string by using the previous one to obtain a higher compression ratio. In this paper, we present a practical implementation of this method suitable for any kinds of data, and show that CSD is more efficient than the LZC which is the method used by the program compress available on UNIX systems. The CSD compression performance was about 10% better than that of LZC with the practical dictionary size, an 8k-entry dictionary when the test data was from the Calgary Compression Corpus. With hashing, the CSD processing speed became as fast as that of LZC, although the CSD algorithm was more complicated than LZC.

16-bit Asian language codes can not be compressed well by conventional 8-bit sampling text compression schemes. Previously, we reported the application of a word-based text compression method that uses 16-bit sampling for the compression of Japanese texts. This paper describes our further efforts in applying a word-based method with a static canonical Huffman encoder to both Japanese and Chinese texts. The method was proposed to support a multilingual environment, as we replaced the word-dictionary and the canonical Huffman code table for the respective language appropriately. A computer simulation showed that this method is effective for both languages. The obtained compression ratio was a little less than 0.5 without regarding the Markov context, and around 0.4 when accounting for the first order Markov context.