Image semantic communication (ISC) has garnered significant attention for its potential to achieve high efficiency in visual content transmission. However, existing ISC systems based on joint source-channel coding face challenges in interpretability, operability, and compatibility. To address these limitations, we propose a novel trustworthy ISC framework. This approach leverages text extraction and segmentation mapping techniques to convert images into explainable semantics, while employing Generative Artificial Intelligence (GenAI) for multiple downstream inference tasks. We also introduce a multi-rate ISC transmission protocol that dynamically adapts to both the received explainable semantic content and specific task requirements at the receiver. Simulation results demonstrate that our framework achieves explainable learning, decoupled training, and compatible transmission in various application scenarios. Finally, some intriguing research directions and application scenarios are identified.

翻译：图像语义通信因其在视觉内容传输中实现高效率的潜力而受到广泛关注。然而，现有基于联合信源信道编码的ISC系统在可解释性、可操作性和兼容性方面面临挑战。为应对这些局限，我们提出了一种新颖的可信ISC框架。该方法利用文本提取与分割映射技术将图像转换为可解释的语义表示，同时采用生成式人工智能执行多种下游推理任务。我们还提出了一种多速率ISC传输协议，该协议能根据接收端获取的可解释语义内容及具体任务需求进行动态适配。仿真结果表明，我们的框架在多种应用场景中实现了可解释学习、解耦训练与兼容传输。最后，本文指出了若干值得探索的研究方向与应用场景。