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The Likelihood Encoder for Lossy Compression

Author(s): Song, Eva C; Cuff, Paul; Poor, Harold V

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Abstract: A likelihood encoder is studied in the context of lossy source compression. The analysis of the likelihood encoder is based on the soft-covering lemma. It is demonstrated that the use of a likelihood encoder together with the soft-covering lemma yields simple achievability proofs for classical source coding problems. The cases of the point-to-point rate-distortion function, the rate-distortion function with side information at the decoder (i.e., the Wyner-Ziv problem), and the multi-terminal source coding inner bound (i.e., the Berger-Tung problem) are examined in this paper. Furthermore, a non-asymptotic analysis is used for the point-to-point case to examine the upper bound on the excess distortion provided by this method. The likelihood encoder is also related to a recent alternative technique using the properties of random binning.
Publication Date: Apr-2016
Citation: Song, Eva C., Paul Cuff, and Harold V. Poor. "The Likelihood Encoder for Lossy Compression." IEEE Transactions on Information Theory 62, no. 4 (2016): pp. 1836-1849. doi:10.1109/TIT.2016.2529657
DOI: doi:10.1109/TIT.2016.2529657
ISSN: 0018-9448
EISSN: 1557-9654
Pages: 1836-1849
Type of Material: Journal Article
Journal/Proceeding Title: IEEE Transactions on Information Theory
Version: Author's manuscript

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