Achievable Second-Order Asymptotics for Successive Refinement Using Gaussian Codebooks
Author：Lin Bai Time：July 14, 2021 Number of clicks：
Language：English
Conference：2021 IEEE International Symposium on Information Theory (ISIT)
Date of Publication：July 14, 2021
Abstract：
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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