A unified approach to source and message compression
A unified approach to source and message compression
复制标题
源和消息压缩的统一方法
DOI：
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发表时间：
2017
期刊：
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通讯作者：
N. Warsi
中科院分区：
文献类型：
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作者：
Anurag Anshu;Rahul Jain;N. Warsi
我们研究了点对点和多方场景（有或没有辅助信息）的一次性设置中的源和消息压缩问题。我们使用[Anshu,Devabathini and Jain 2014]中引入的凸分割技术和[Anshu, Jain and Warsi 2017]中引入的基于位置的解码技术，以统一的方式得出这些任务的可实现性结果，后者又使用分布之间的假设检验。这些结果是根据平滑最大散度和平滑假设检验散度得出的。作为这项工作中研究的任务的副产品，我们在一次性情况下获得了几个已知的源压缩结果（最初是在渐近和独立同分布设置中研究的）。
我们的可实现结果之一包括带有辅助信息的消息压缩问题，最初在 [Braverman and Rao 2011] 中进行了研究。通过证明逆界，我们表明我们的结果和 [Braverman and Rao 2011] 中的结果在一次性设置中都接近最优。
We study the problem of source and message compression in the one-shot setting for the point-to-point and multi-party scenarios (with and without side information). We derive achievability results for these tasks in a unified manner, using the techniques of convex-split, which was introduced in [Anshu,Devabathini and Jain 2014] and position-based decoding introduced in [Anshu, Jain and Warsi 2017], which in turn uses hypothesis testing between distributions. These results are in terms of smooth max divergence and smooth hypothesis testing divergence. As a by-product of the tasks studied in this work, we obtain several known source compression results (originally studied in the asymptotic and i.i.d. setting) in the one-shot case.
One of our achievability results includes the problem of message compression with side information, originally studied in [Braverman and Rao 2011]. We show that both our result and the result in [Braverman and Rao 2011] are near optimal in the one-shot setting by proving a converse bound.