Segment Anything Meets Semantic Communication
Shehbaz Tariq Brian Estadimas Arfeto Chaoning Zhang Hyundong Shin
In light of the diminishing returns of traditional methods for enhancing
transmission rates, the domain of semantic communication presents promising new
frontiers. Focusing on image transmission, this paper explores the application
of foundation models, particularly the Segment Anything Model (SAM) developed
by Meta AI Research, to improve semantic communication. SAM is a promptable
image segmentation model that has gained attention for its ability to perform
zero-shot segmentation tasks without explicit training or domain-specific
knowledge. By employing SAM's segmentation capability and lightweight neural
network architecture for semantic coding, we propose a practical approach to
semantic communication. We demonstrate that this approach retains critical
semantic features, achieving higher image reconstruction quality and reducing
communication overhead. This practical solution eliminates the
resource-intensive stage of training a segmentation model and can be applied to
any semantic coding architecture, paving the way for real-world applications.