高阶熵静态模型的快速压缩

L. Foschini, R. Grossi, Ankur Gupta, J. Vitter
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引用次数: 15

摘要

我们报告了一种称为wzip的简单编码格式,用于解压缩块排序转换,例如Burrows-Wheeler转换(BWT)。我们的压缩器使用简单的伽玛编码和RLE概念,用小波树组织,在更短的时间内获得比bzip2稍好的压缩比。事实上,我们的压缩/解压缩时间依赖于H/下标H/, H阶经验熵。这种性能与数据可压缩性的关系是压缩算法中一个重要的新思想。我们的压缩机的另一个重要贡献是它的简单性。我们的压缩器还可以作为具有少量数据的全文索引运行,同时仍然保留与压缩器的向后兼容性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Fast compression with a static model in high-order entropy
We report on a simple encoding format called wzip for decompressing block-sorting transforms, such as the Burrows-Wheeler transform (BWT). Our compressor uses the simple notions of gamma encoding and RLE, organized with a wavelet tree, to achieve a slightly better compression ratio than bzip2 in less time. In fact, our compression/decompression time is dependent on H/sub h/, the hth order empirical entropy. This relationship of performance to the compressibility of data is a key new idea among compression algorithms. Another key contribution of our compressor is its simplicity. Our compressor can also operate as a full-text index with a small amount of data, while still preserving backward compatibility with just the compressor.
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