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arXiv 1002.2971cs.ITmath.IT
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擦除多个说明

Erasure Multiple Descriptions

Ebad Ahmed, Aaron B. Wagner

摘要:

我们考虑一个二进制擦除版本的n通道多描述问题的对称描述,即,N个描述的速率是相同的,并且失真约束仅取决于接收到的消息的数量。我们考虑的情况下,没有多余的n个描述的每k。我们的目标是表征可实现的失真D_1,D_2,.,D_n。我们使用两个失真标准来测量重建的保真度:一个平均情况下的失真标准,根据该标准,失真是通过对所有源序列的每个字母的失真的平均值来测量的,和一个最坏情况下的失真标准,根据该标准,失真是通过对所有源序列的每个字母的失真的最大值来测量的。我们提出的可扩展性计划,基于随机装箱的平均情况下的失真和系统的MDS(最大距离可分离)码的最坏情况下的失真,并证明相应的可实现的失真区域的最优结果。然后,我们使用二进制擦除多描述设置,提出了一个分层编码框架的多描述,然后我们应用到矢量高斯多描述,并证明其最优性对称标量高斯多描述与两个级别的接收器和没有多余的中央接收器的速率。我们还证明了一般的多终端信源编码问题的一个新的外边界,并使用它来证明鲁棒二进制擦除CEO问题的最优性结果。对于后者,我们为任何编码方案的\ell消息的失真提供了一个严格的下限,该编码方案实现了k个消息的最小可实现失真,其中k小于或等于\ell。

英文摘要:

We consider a binary erasure version of the n-channel multiple descriptions problem with symmetric descriptions, i.e., the rates of the n descriptions are the same and the distortion constraint depends only on the number of messages received. We consider the case where there is no excess rate for every k out of n descriptions. Our goal is to characterize the achievable distortions D_1, D_2,...,D_n. We measure the fidelity of reconstruction using two distortion criteria: an average-case distortion criterion, under which distortion is measured by taking the average of the per-letter distortion over all source sequences, and a worst-case distortion criterion, under which distortion is measured by taking the maximum of the per-letter distortion over all source sequences. We present achievability schemes, based on random binning for average-case distortion and systematic MDS (maximum distance separable) codes for worst-case distortion, and prove optimality results for the corresponding achievable distortion regions. We then use the binary erasure multiple descriptions setup to propose a layered coding framework for multiple descriptions, which we then apply to vector Gaussian multiple descriptions and prove its optimality for symmetric scalar Gaussian multiple descriptions with two levels of receivers and no excess rate for the central receiver. We also prove a new outer bound for the general multi-terminal source coding problem and use it to prove an optimality result for the robust binary erasure CEO problem. For the latter, we provide a tight lower bound on the distortion for \ell messages for any coding scheme that achieves the minimum achievable distortion for k messages where k is less than or equal to \ell.

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