Achievable Second-Order Asymptotics for Successive Refinement Using Gaussian Codebooks
作者：白琳 时间：2021年07月14日 21:19 点击数：
语言：English
发表会议：2021 IEEE International Symposium on Information Theory (ISIT)
发表日期：2021年7月14日
摘要：
We study the mismatched successive refinement problem where one uses a fixed code to compress an arbitrary source with random Gaussian codebooks and minimum Euclidean distance encoding in a successive manner. Specifically, we generalize the mismatched rate-distortion framework by Lapidoth (T-IT, 1997) to the successive refinement setting and derive the achievable second-order asymptotics. Our result implies that any source that satisfies a mild moment constraint is successive refinable under our code. Furthermore, our proof, when specialized to a Gaussian memoryless source, provides an alternative achievability proof with structured codebooks for the successive refinement problem, which was studied by Zhou, Tan, Motani (T-IT, 2018) where a covering lemma without specifying the locations of codewords was used.
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