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arXiv 1505.03337cs.ITmath.IT
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整数维奇异随机变量的熵与信源编码

Entropy and Source Coding for Integer-Dimensional Singular Random Variables

Günther Koliander, Georg Pichler, Erwin Riegler, Franz Hlawatsch

摘要:

熵和微分熵是信息论中的重要量。一个易于处理的扩展到奇异随机变量,既不是离散的,也不是连续的,迄今为止还没有。在这里,我们提出了这样一个扩展的实际相关类的整数维奇异随机变量。所提出的熵定义包含离散随机变量的熵和连续随机变量的微分熵作为特例。我们表明,它在Lipschitz函数下以自然的方式变换,并且在酉变换下是不变的。我们定义了整数维奇异随机变量的联合熵和条件熵,并证明了所提出的熵表达了有用的互信息表达式。作为我们的熵定义的第一个应用程序,我们提出了一个结果的最小预期码字长度的量化整数维奇异源和香农下界的整数维奇异源。

英文摘要:

Entropy and differential entropy are important quantities in information theory. A tractable extension to singular random variables-which are neither discrete nor continuous-has not been available so far. Here, we present such an extension for the practically relevant class of integer-dimensional singular random variables. The proposed entropy definition contains the entropy of discrete random variables and the differential entropy of continuous random variables as special cases. We show that it transforms in a natural manner under Lipschitz functions, and that it is invariant under unitary transformations. We define joint entropy and conditional entropy for integer-dimensional singular random variables, and we show that the proposed entropy conveys useful expressions of the mutual information. As first applications of our entropy definition, we present a result on the minimal expected codeword length of quantized integer-dimensional singular sources and a Shannon lower bound for integer-dimensional singular sources.

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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