A novel distributed source coding model which named semantic-aware multi-terminal (MT) source coding is proposed and investigated in the paper, where multiple agents independently encode an imperceptible semantic source, while both semantic and observations are reconstructed within their respective fidelity criteria. We start from a generalized single-letter characterization of sum rate-distortion region of this problem. Furthermore, we propose a mixed MSE-Log loss framework for this model and specifically depict the rate-distortion bounds when sources are Gaussian mixture distributed. For this case, we first present a relative tight outer bound and explore the activeness of semantic and observation distortion constraints, in which we find that good observation reconstruction will not incur too much semantic errors, but not vice versa. Moreover, we provide a practical coding scheme functioning as an achievable regime of inner bound with the performance analysis and simulation results, which verifies the feasibility of the idea "detect and compress" for Gaussian mixture sources. Our results provide theoretical instructions on the fundamental limits and can be used to guide the practical semantic-aware coding designs for multi-user scenarios.

翻译：本文提出并研究了一种名为语义感知多终端（MT）源编码的新型分布式源编码模型。在该模型中，多个智能体独立编码一个不可感知的语义源，同时以各自的保真度准则重建语义源和观测数据。我们首先给出了该问题率失真区域的广义单字母表征。进一步，针对该模型提出了混合均方误差-对数损失框架，并具体刻画了当源服从高斯混合分布时的率失真界。针对该情形，我们首先给出了一个相对紧的外界，并探讨了语义与观测失真约束的活跃性，发现良好的观测重建不会引入过多的语义误差，反之则不然。此外，我们提供了一种实用的编码方案，该方案作为内界的可达区域，并进行了性能分析与仿真验证，证实了“检测与压缩”思想用于高斯混合源的可行性。我们的研究结果为语义感知编码在基本极限方面提供了理论指导，并可应用于多用户场景中实用的语义感知编码设计。