Deep Joint Source-Channel Coding for Wireless Image Transmission with Semantic Importance
Qizheng Sun, Caili Guo, Yang Yang, Jiujiu Chen, Rui Tang, Chuanhong Liu
Abstract
The sixth-generation mobile communication system proposes the vision of smart interconnection of everything, which requires accomplishing communication tasks while ensuring the performance of intelligent tasks. A joint source-channel coding method based on semantic importance is proposed, which aims at preserving semantic information during wireless image transmission and thereby boosting the performance of intelligent tasks for images at the receiver. Specifically, we first propose semantic importance weight calculation method, which is based on the gradient of intelligent task’s perception results with respect to the features. Then, we design the semantic loss function in the way of using semantic weights to weight the features. Finally, we train the deep joint source-channel coding network using the semantic loss function. Experiment results demonstrate that the proposed method achieves up to 57.7% and 9.1% improvement in terms of intelligent task’s performance compared with the source-channel separation coding method and the deep source-channel joint coding method without considering semantics at the same compression rate and signal-to-noise ratio, respectively.