Segment Anything Meets Semantic Communication

TL;DR

This paper leverages the Segment Anything Model (SAM) for semantic image communication, enhancing reconstruction quality and reducing transmission overhead without extensive training, thus enabling practical real-world applications.

Contribution

It introduces a novel approach combining SAM's zero-shot segmentation with lightweight semantic coding for efficient semantic communication.

Findings

Higher image reconstruction quality achieved

Reduced communication overhead demonstrated

Eliminates resource-intensive training stage

Abstract

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.