On Splitting Lightweight Semantic Image Segmentation for Wireless Communications

TL;DR

This paper introduces a splitting method for semantic image segmentation in wireless communications, reducing bandwidth and computational load at resource-limited devices while maintaining high segmentation accuracy, suitable for 6G systems.

Contribution

It proposes a novel splitting approach that balances computational efficiency and bandwidth use in semantic image segmentation for resource-constrained environments.

Findings

Up to 72% reduction in bit rate during transmission.

Over 19% reduction in computational load at the transmitter.

Maintains segmentation accuracy comparable to full models.

Abstract

Semantic communication represents a promising technique towards reducing communication costs, especially when dealing with image segmentation, but it still lacks a balance between computational efficiency and bandwidth requirements while maintaining high image segmentation accuracy, particularly in resource-limited environments and changing channel conditions. On the other hand, the more complex and larger semantic image segmentation models become, the more stressed the devices are when processing data. This paper proposes a novel approach to implementing semantic communication based on splitting the semantic image segmentation process between a resource constrained transmitter and the receiver. This allows saving bandwidth by reducing the transmitted data while maintaining the accuracy of the semantic image segmentation. Additionally, it reduces the computational requirements at the resource constrained transmitter compared to doing all the semantic image segmentation in the transmitter. The proposed approach is evaluated by means of simulation-based experiments in terms of different metrics such as computational resource usage, required bit rate and segmentation accuracy. The results when comparing the proposal with the full semantic image segmentation in the transmitter show that up to 72% of the bit rate was reduced in the transmission process. In addition, the computational load of the transmitter is reduced by more than 19%. This reflects the interest of this technique for its application in communication systems, particularly in the upcoming 6G systems.