Deep learning based progressive joint source-channel coding for wireless image transmission

Deep learning-based Joint Source-Channel Coding (JSCC) is a crucial component in semantic communication, and recent research has made significant progress in adapting to different channels. In this paper, we propose a multi-stage progressive technique called Deep learning based Progressive Joint Source-Channel Coding (DP-JSCC). This approach partitions the source into multiple stages and transmits the signals continuously. The receiver gradually enhances the quality of image reconstruction by progressively receiving the signals, offering greater flexibility compared to existing dynamic rate transmission methods. The model adopts a lightweight architectural design, where we introduce an efficient module called the Inverted Shuffle Attention Bottleneck (ISAB) and incorporate self-attention mechanisms in the encoding and decoding process to capture signal correlations and establish long-range dependencies. Additionally, we introduce the Progressive Focus Weight Allocation (PFWA) method to improve the image reconstruction capability in progressive transmission tasks. These design enhance the expressive capacity of the model. Simulation results demonstrate that DP-JSCC can flexibly adjust the transmission rate according to requirements without the need for retraining or deployment, enabling continuous optimization of signals at different rates. Furthermore, compared to state-of-the-art JSCC methods, DP-JSCC exhibits advantages in terms of computational complexity, parameter count, and reconstruction performance.