Home
Scholarly Works
Source-Channel Separation Theorems for Distortion...
Conference

Source-Channel Separation Theorems for Distortion Perception Coding

Abstract

It is well known that separation between lossy source coding and channel coding is asymptotically optimal under classical additive distortion measures. Recently, coding under a new class of quality considerations, often referred to as perception or realism, has attracted significant attention due to its close connection to neural generative models and semantic communications. In this work, we revisit source-channel separation under the consideration of distortion-perception. We show that when the perception quality is measured on the block level, i.e., in the strong sense, the optimality of separation still holds when common randomness is shared between the encoder and the decoder; however, separation is no longer optimal when such common randomness is not available. In contrast, when the perception quality is the average per-symbol measure, i.e., in the weak sense, the optimality of separation holds regardless of the availability of common randomness.

Authors

Tian C; Chen J; Narayanan K

Volume

00

Pagination

pp. 1-6

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Publication Date

June 27, 2025

DOI

10.1109/isit63088.2025.11195371

Name of conference

2025 IEEE International Symposium on Information Theory (ISIT)

Labels

View published work (Non-McMaster Users)

Contact the Experts team