Year: 2016
Type: article
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.
Authors: Eva C. Song, Paul Cuff, H. Vincent Poor
Institution: Princeton University
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
Awards: CIF: Small: Collaborative Research: Towards universal signal recovery algorithms, CIF: Small: Causal Secrecy-A Theoretical Basis for Secrecy of Signals, Collaborative Research: EARS: Fundamental Limits of Spectrum Sensing, G4499113007, G7247502720, CAREER: Digital Encoding of Information Signals for Security with Limited Resources
OpenAlex ID: W2139352051