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

机译:Shannon何时在有限的BlockLength中缩小?

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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Shannon的下限的抽象版,适用于抽象来源和任意失真措施,并恢复古典香农下限作为特殊情况。提出了一个完全达到的必要和充分条件。据证明,每当满足该条件时,源的D倾斜信息都采用简单,明确的表示,使Shannon的下限。方便的表示简化了可实现的率失真权衡的非渐近分析。特别是,如果无记忆源符合Shannon的平等界限,则其速率 - 色散函数仅通过源的常规给出。

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