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arXiv 1607.07003cs.ITmath.IT
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基于独立分量分析的大字母信源编码

Large Alphabet Source Coding using Independent Component Analysis

Amichai Painsky, Saharon Rosset, Meir Feder

摘要:

大字母表信源编码是数据压缩中的一个基本问题。它在自然语言文本、语音和图像的压缩等方面有着广泛的应用。最常用的方法的经典观点是,最好用一个字母表来描述源,该字母表至少与观察到的字母表一样大。在这项工作中,我们挑战这种方法,并介绍了一个概念框架,其中一个大的字母源被分解成“尽可能统计独立”的组件。这种分解使我们能够分别对每个分量应用熵编码,同时受益于它们减小的字母表大小。我们表明,在许多情况下,这样的分解结果在一个总和的边际熵,这只是略大于源的熵。我们建议的算法,基于二进制独立分量分析的推广,适用于各种大字母信源编码设置。这包括经典的无损压缩、通用压缩和高维矢量量化。在这些设置中,我们建议的方法优于最常用的方法。此外,我们提出的框架在大多数情况下更容易实现。

英文摘要:

Large alphabet source coding is a basic and well-studied problem in data compression. It has many applications such as compression of natural language text, speech and images. The classic perception of most commonly used methods is that a source is best described over an alphabet which is at least as large as the observed alphabet. In this work we challenge this approach and introduce a conceptual framework in which a large alphabet source is decomposed into "as statistically independent as possible" components. This decomposition allows us to apply entropy encoding to each component separately, while benefiting from their reduced alphabet size. We show that in many cases, such decomposition results in a sum of marginal entropies which is only slightly greater than the entropy of the source. Our suggested algorithm, based on a generalization of the Binary Independent Component Analysis, is applicable for a variety of large alphabet source coding setups. This includes the classical lossless compression, universal compression and high-dimensional vector quantization. In each of these setups, our suggested approach outperforms most commonly used methods. Moreover, our proposed framework is significantly easier to implement in most of these cases.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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