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Title:
Low-Latency Rate-Distortion-Perception Trade-offs Through Randomized Distributed Function Computations
Authors: Onur Gunlu, Maciej Skorski, H. Vincent Poor
Abstract:Semantic communication systems focus on the transmission of meaning rather than the exact reconstruction of data, reshaping communication network design to achieve transformative efficiency in latency-sensitive and bandwidth-limited scenarios. Within this context, we investigate the rate-distortion-perception (RDP) problem for image compression, which plays a central role in producing perceptually realistic outputs under rate constraints. By employing the randomized distributed function computation (RDFC) framework, we derive an achievable non-asymptotic RDP region that captures the finite blocklength trade-offs among rate, distortion, and perceptual quality, aligning with the objectives of semantic communications. This region is further generalized to include either side information or a secrecy requirement, the latter ensuring strong secrecy against eavesdroppers through physical-layer security mechanisms and maintaining robustness in the presence of quantum-capable adversaries. The main contributions of this work are: (i) achievable bounds for non-asymptotic RDP regions subject to realism and distortion constraints; (ii) extensions to cases where side information is available at both the encoder and decoder; (iii) achievable, nonasymptotic RDP bounds with strong secrecy guarantees; (iv) characterization of the asymptotic secure RDP region under a perfect realism constraint; and (v) demonstrations of significant rate reductions and the effects of finite blocklengths, side information, and secrecy constraints. These findings offer concrete guidelines for the design of low-latency, secure, and high-fidelity image compression and generative modeling systems that generate realistic outputs, with relevance for, e.g., privacy-critical applications.
ePrint: https://eprint.iacr.org/2025/1612
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