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When is Shannon's lower bound tight at finite blocklength?

机译:香农的下限什么时候在有限的块长上紧缩?

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This paper formulates an abstract version of Shannon's lower bound that applies to abstract sources and arbitrary distortion measures and that recovers the classical Shannon lower bound as a special case. A necessary and sufficient condition for it to be attained exactly is presented. It is demonstrated that whenever that condition is met, the d-tilted information of the source adopts a simple, explicit representation that parallels Shannon's lower bound. That convenient representation simplifies the non-asymptotic analysis of achievable rate-distortion tradeoffs. In particular, if a memoryless source meets Shannon's lower bound with equality, then its rate-dispersion function is given simply by the varentropy of the source.
机译:本文制定了香农下界的抽象版本,该抽象版本适用于抽象源和任意失真度量,并在特殊情况下恢复了经典的香农下界。提出了精确实现该目标的必要和充分条件。结果表明,只要满足该条件,源的d倾斜信息都将采用与Shannon的下限相似的简单显式表示。这种方便的表示简化了可实现的速率-失真折衷的非渐近分析。特别是,如果一个无记忆的源相等地满足Shannon的下限,则其速率离散函数将简单地由源的变熵给出。

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