单个序列的因果编码与Lempel-Ziv微分熵

T. Linder, R. Zamir
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引用次数: 0

摘要

在因果源编码中,重构被限制为当前和过去源样本的函数,而变长码流可能是非因果的。Neuhoff和Gilbert[1982]表明,对于无记忆源,所有因果有损源代码的最佳性能是通过分时最多两个无记忆码(标量量化器),然后是熵编码来实现的。我们将这一结果推广到小失真极限下单个序列的因果编码。在这种情况下,有限记忆可变速率因果码的最佳性能表现为微分熵的确定性模拟,我们称之为“Lempel-Ziv微分熵”。作为副产品,我们还提供了速率失真函数的香农下界的单个序列版本。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Causal coding of individual sequences and the Lempel-Ziv differential entropy
In causal source coding, the reconstruction is restricted to be a function of the present and past source samples, while the variable-length code stream may be noncausal. Neuhoff and Gilbert [1982] showed that for memoryless sources, optimum performance among all causal lossy source codes is achieved by time-sharing at most two memoryless codes (scalar quantizers) followed by entropy coding. We extend this result to causal coding of individual sequences in the limit of small distortion. The optimum performance of finite-memory variable-rate causal codes in this setting is characterized by a deterministic analogue of differential entropy, which we call "Lempel-Ziv differential entropy." As a by-product, we also provide an individual-sequence version of the Shannon lower bound to the rate-distortion function.
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