Semantic Communication for Efficient Image Transmission Tasks based on Masked Autoencoders
Jiale Wu, Celimuge Wu, Yangfei Lin, Jingjing Bao, Zhaoyang Du, Lei Zhong, Xianfu Chen, Yusheng Ji · 2023
Semantic communication, a promising candidate for 6G technology, has become a research hot spot. However, existing studies tend to focus more on image reconstruction rather than accurately transmitting semantic information at the pixel level. This paper introduces a novel approach using codec-based Masked AutoEncoders (MAE) for efficient image transmission. The proposed system compresses local information into low-dimensional latent vectors, improving system efficiency. We also design a selective module for enhanced image reconstruction and implement Noise Adversarial Training (NAT) to increase the system’s resilience to channel noise. Experimental results show that our method effectively improves downstream tasks while preserving image quality.