Trustworthy Image Semantic Communication with GenAI: Explainablity, Controllability, and Efficiency
This work addresses the problem of making image semantic communication more reliable and adaptable for applications requiring efficient visual content transmission, representing an incremental improvement over existing methods.
The paper tackles the challenges of interpretability, operability, and compatibility in image semantic communication systems by proposing a trustworthy framework that uses text extraction, segmentation mapping, and GenAI for inference tasks, achieving explainable learning, decoupled training, and compatible transmission in simulations.
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.