Prediction-based compression methods, like prediction by partial matching, achieve a remarkable compression ratio, especially for texts written in natural language. However, they are not efficient in terms of speed. Part of the problem concerns the usage of dynamic entropy encoding, which is considerably slower than the static alternatives. In this paper, we propose a prediction-based compression method that decouples the context model from the frequency model. The separation allows static entropy encoding to be used without a significant overhead in the meta-data embedded in the compressed data. The result is a reasonably efficient algorithm that is particularly suited for small textual files, as the experiments show. We also show it is relatively easy to built strategies designed to handle specific cases, like the compression of files whose symbols are only locally frequent.

中文翻译：

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LUISA：在基于预测的压缩中将频率模型与上下文模型分离

基于预测的压缩方法（例如通过部分匹配进行的预测）可实现显着的压缩率，尤其是对于以自然语言编写的文本而言。但是，它们在速度方面并不高效。问题的一部分与动态熵编码的使用有关，该方法比静态替代方法要慢得多。在本文中，我们提出了一种基于预测的压缩方法，该方法将上下文模型与频率模型解耦。分离允许使用静态熵编码，而不会在压缩数据中嵌入的元数据中产生大量开销。如实验所示，结果是一种合理有效的算法，特别适合于小型文本文件。我们还表明，为处理特定情况而制定策略相对容易，