Idempotent Semantic Communication Against Distortion Accumulation

Despite the remarkable success of semantic image transmission, existing approaches face the challenge of distortion accumulation. Specifically, as a received image is further forwarded to other devices, reconstruction distortion will accumulate, leading to decreased system stability. In this paper, we propose an idempotent semantic communication system for image transmission to enhance stability. We systematically analyze the factors contributing to this accumulation effect and propose several strategies to mitigate it. First, we design the system using a right-invertible neural network to achieve idempotence, ensuring the decoder functions as the right inverse of the encoder. Second, we introduce a feature discretization mechanism to further reduce distortion accumulation, leveraging the benefits of digitalization over analog transmission. Finally, we employ a recursive training strategy, which incorporates the reconstructed images into the training process to significantly improve overall stability. Empirical results demonstrate that our proposed strategies effectively enhance system stability, minimizing quality degradation and enhancing output consistency across multiple transmissions.