Search Results (3)

Semantic communication frameworks aim to convey the underlying significance of data rather than reproducing it exactly, a perspective that enables substantial efficiency gains in settings constrained by latency or bandwidth. Motivated by this shift, we study the rate–distortion–perception (RDP) trade-off for image compression, a setting in which reconstructions must be not only accurate but also perceptually faithful. Our analysis is carried out through the lens of randomized distributed function computation (RDFC) framework, which provides a principled means of synthesizing randomness and shaping output distributions. Leveraging this framework, we establish finite-blocklength characterizations of the RDP region, quantifying how communication rate, distortion, and perceptual fidelity interact in non-asymptotic regimes. We further broaden this characterization by incorporating two practically relevant extensions: (i) scenarios in which encoder and decoder share side information, and (ii) settings that require strong secrecy guarantees against adversaries, which might include those with quantum capabilities. Moreover, we identify the corresponding asymptotic region under a perfect realism constraint and examine how side information, finite blocklength effects, and secrecy demands influence achievable performance. The resulting insights provide actionable guidance for the development of low-latency, secure, and realism-aware image compression and generative modeling systems. Full article

Traditional rate–distortion (RD) theory examines the trade-off between the average length of the compressed representation of a source and the additive distortions of its reconstruction. The rate–distortion–perception (RDP) framework, which integrates the perceptual dimension into the RD paradigm, has garnered significant attention due to recent advancements in machine learning, where perceptual fidelity is assessed by the divergence between input and reconstruction distributions. In communication systems where downstream tasks involve generative modeling, high perceptual fidelity is essential, despite distortion constraints. However, while zero distortion implies perfect realism, the converse is not true, highlighting an imbalance in the significance of distortion and perceptual constraints. This article clarifies that incorporating perceptual constraints does not decrease the necessary rate; instead, under certain conditions, additional rate is required, even with the aid of common and private randomness, which are key elements in generative models. Consequently, we project an increase in expected traffic in intelligent communication networks with the consideration of perceptual quality. Nevertheless, a modest increase in rate can enable generative models to significantly enhance the perceptual quality of reconstructions. By exploring the synergies between generative modeling and communication through the lens of information-theoretic results, this article demonstrates the benefits of intelligent communication systems and advocates for the application of the RDP framework in advancing compression and semantic communication research. Full article

We consider the problem of learned speech transmission. Existing methods have exploited joint source–channel coding (JSCC) to encode speech directly to transmitted symbols to improve the robustness over noisy channels. However, the fundamental limit of these methods is the failure of identification of content diversity across speech frames, leading to inefficient transmission. In this paper, we propose a novel neural speech transmission framework named NST. It can be optimized for superior rate–distortion–perception (RDP) performance toward the goal of high-fidelity semantic communication. Particularly, a learned entropy model assesses latent speech features to quantify the semantic content complexity, which facilitates the adaptive transmission rate allocation. NST enables a seamless integration of the source content with channel state information through variable-length joint source–channel coding, which maximizes the coding gain. Furthermore, we present a streaming variant of NST, which adopts causal coding based on sliding windows. Experimental results verify that NST outperforms existing speech transmission methods including separation-based and JSCC solutions in terms of RDP performance. Streaming NST achieves low-latency transmission with a slight quality degradation, which is tailored for real-time speech communication. Full article