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arXiv 2103.16901cs.ITmath.IT
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强数据处理和优控制不等式及其在编码问题中的应用

On Strong Data-Processing and Majorization Inequalities with Applications to Coding Problems

Igal Sason

摘要:

这项工作提供了数据处理和优化不等式的$f$ -分歧,它认为他们的一些应用程序的编码问题。这项工作还提供了一个离散随机变量的函数的Rényi熵与有限个可能的值,其中所考虑的功能是不是一对一的紧界,和他们的推导是基于优化和Schur-Schurt的Rényi熵。$f$ -发散不等式的一个应用是指列表解码的性能分析,无论是固定的或可变的列表大小,列表解码错误概率上的一些早期的界限,在一个统一的方式再现,并获得新的界限,并举例说明数字。另一个应用是关于近似的概率质量函数,这是由汤斯顿树的叶子,由等概率分布的质量的研究。进一步分析了有限长Tunstall码的压缩率,以证明其与无记忆平稳离散信源的香农熵的接近程度。考虑到Rényi熵的严格界和Campbell的工作,导出了离散无记忆信源的无损数据压缩的非渐近界.

英文摘要:

This work provides data-processing and majorization inequalities for $f$-divergences, and it considers some of their applications to coding problems. This work also provides tight bounds on the Rényi entropy of a function of a discrete random variable with a finite number of possible values, where the considered function is not one-to-one, and their derivation is based on majorization and the Schur-concavity of the Rényi entropy. One application of the $f$-divergence inequalities refers to the performance analysis of list decoding with either fixed or variable list sizes; some earlier bounds on the list decoding error probability are reproduced in a unified way, and new bounds are obtained and exemplified numerically. Another application is related to a study of the quality of approximating a probability mass function, which is induced by the leaves of a Tunstall tree, by an equiprobable distribution. The compression rates of finite-length Tunstall codes are further analyzed for asserting their closeness to the Shannon entropy of a memoryless and stationary discrete source. In view of the tight bounds for the Rényi entropy and the work by Campbell, non-asymptotic bounds are derived for lossless data compression of discrete memoryless 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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