On random binning versus conditional codebook methods in multiple descriptions coding

摘要

There are two common types of encoding paradigms in multiple descriptions (MD) coding: i) an approach based on conditional codebook generation, which was originally initiated by El-Gamal and Cover for the 2 channel setting and later extended to more than 2 channels by Venkataramani, Kramer and Goyal (VKG), ii) and an approach based on Slepian and Wolf's random binning technique, proposed by Pradhan, Puri and Ramchandran (PPR) for L > 2 descriptions. It is well known that the achievable region due to PPR subsumes the VKG region for the symmetric Gaussian MD problem. Motivated by several practical advantages of random binning based methods over the conditional codebook encoding, this paper focuses on the following important questions: Does a random binning based scheme achieve the performance of conditional codebook encoding, even for the 2 descriptions scenario? Are random binning based approaches beneficial for settings that are not fully symmetric? This paper answers both these questions in the affirmative. Specifically, we propose a 2 descriptions coding scheme, based on random binning, which subsumes the currently known largest region for this problem due to Zhang and Berger. Moreover, we propose its extensions to L > 2 channels and derive the associated achievable regions. The proposed scheme enjoys the advantages of both encoding paradigms making it particularly useful when there is symmetry only within a subset of the descriptions.