2020 IEEE International Symposium
on Information Theory
21-26 June 2020 • Los Angeles, California, USA
| Paper ID | O.3.2 | ||
| Paper Title | Improved Achievable Regions in Networked Scalable Coding Problems | ||
| Authors | Emrah Akyol, Binghamton University, United States; Urbashi Mitra, University of Southern California, United States; Ertem Tuncel, UC Riverside, United States; Kenneth Rose, UC Santa Barbara, United States | ||
| Session | O.3: Multi-terminal Source Coding I | ||
| Presentation | Lecture | ||
| Track | Source Coding | ||
| Manuscript | Click here to download the manuscript | ||
| Virtual Presentation | Click here to watch in the Virtual Symposium | ||
| Abstract | In this paper, we present new results on the achievable rate-distortion regions in networked scalable compression problems, based on a flexible codebook generation and binning method. First, we consider the problem of scalable coding in the presence of decoder side information, for which the prior work analyzed the two important cases the degraded side information where source $X$ and the side information variables ($Y_1, Y_2$) form a Markov chain in the order of either $X-Y_1-Y_2$ or $X-Y_2-Y_1$. First, we present an example non-Markov side information scenario where the proposed coding strategy achieves a strictly larger rate-distortion region compared to prior work. We then consider the problem of multi-user successive refinement where different users that are connected to a central server via links with different noiseless capacities strive to reconstruct the source in a progressive fashion. It is shown that a prior rate-distortion region is suboptimal in general, albeit its optimality for a Gaussian source with MSE distortion, and the proposed coding scheme achieves points beyond the achievable region of prior work. | ||
Plan Ahead
2021 IEEE International Symposium on Information Theory
11-16 July 2021 | Melbourne, Victoria, Australia
