Task-Oriented Semantic Communication Systems Based on Extended Rate-Distortion Theory

Considering the performance of intelligent task during signal exchange can help the communication system to automatically select those semantic parts which are helpful to perform the target task for compression and reconstruction, which can both greatly reduce the redundancy in signal and ensure the performance of the task. The traditional communication system based on rate-distortion theory treats all the information in the signal equally, but ignores their different importance to accomplish the task, which leads to waste of communication resources. In this paper, combined with the information bottleneck method, we present an extended rate-distortion theory which considers both concise representation and semantic distortion. Based on this theory, a task-oriented semantic image communication system is proposed. In order to verify that the proposed system can achieve performance improvement on different intelligent tasks, we apply the basic system trained with classification task to the system with object detection as the target task. The experimental results demonstrate that the proposed method outperforms the traditional and multi-task based communication system in terms of task performance at the same signal compression degree and noise interference degree. Furthermore, it is necessary to consider a compromise between rate-distortion theory and information bottleneck theory by comparing the pure rate-distortion scheme and the pure IB scheme.