Segment Anything Meets Semantic Communication
This provides a practical solution for semantic communication in image transmission by eliminating resource-intensive training, though it is incremental as it adapts an existing model.
This paper tackles the problem of improving image transmission efficiency in semantic communication by applying the Segment Anything Model (SAM) for segmentation without training, resulting in higher reconstruction quality and reduced communication overhead.
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.