利用字典改进PPM算法
Improving PPM Algorithm Using Dictionaries
摘要：
提出了一种改进传统的基于字符的PPM文本压缩算法的方法。将文本文件看作是一个单词和非单词交替排列的序列，该算法的基本思想是使用基于字符的上下文模型对非单词和单词前缀进行编码，使用字典模型对单词后缀进行编码。该算法利用字典模型，将多个字符作为一个整体进行编码，从而提高了压缩效率。该算法的优点是：1）不需要任何文本预处理; 2）不需要任何显式码字来识别上下文和字典模型之间的切换; 3）可以应用于任何基于字符的PPM算法，而不会产生太多额外的计算开销。测试结果表明，该方法比基于字符的PPM方法有明显的改进，尤其是在低阶情况下。
英文摘要：
We propose a method to improve traditional character-based PPM text compression algorithms. Consider a text file as a sequence of alternating words and non-words, the basic idea of our algorithm is to encode non-words and prefixes of words using character-based context models and encode suffixes of words using dictionary models. By using dictionary models, the algorithm can encode multiple characters as a whole, and thus enhance the compression efficiency. The advantages of the proposed algorithm are: 1) it does not require any text preprocessing; 2) it does not need any explicit codeword to identify switch between context and dictionary models; 3) it can be applied to any character-based PPM algorithms without incurring much additional computational cost. Test results show that significant improvements can be obtained over character-based PPM, especially in low order cases.
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