Adaptive Semantic Image Transmission Using Generative Foundation Model
Peiwen Jiang, Chao-Kai Wen, Xiao Li, Shi Jin · 2024
Semantic communication has shown promise in improving data transmission efficiency under critical scenarios, particularly through the use of foundation models (FMs). This study introduces FMSC, a semantic communication framework based on FMs. The framework employs FM-based segmentation and reconstruction to significantly reduce bandwidth requirements and accurately recover semantic features in noisy and interfered environments. To address varying scenarios, an adaptive encoder-decoder is proposed to protect important features and satisfy user requirements. Particularly, the proposed method can leverage a well-received image as a reference for repairing damaged images.