A Quality Metric for Semantically Transmitted Images in Machine-to-Machine Communications

Abstract

Semantic communications focus on transmitting information that encapsulates meaning, enabling both machines and humans to understand the intended message with greater accuracy. Unlike traditional communication systems, which send data without considering its semantic value, this approach prioritises the content's meaning and requires a novel metric to gauge semantic quality. Our framework integrates a specialised Vision Transformer (ViT) for semantic segmentation, named SemExT, at the transmission end and a pre-trained Generative Adversarial Network (GAN) for image reconstruction at the receiving end. The system's effectiveness is evaluated by comparing the semantic content of the reconstructed image with the original, using Deceptron2, an advanced object detection model. This comparison establishes a new metric for assessing the quality of semantic transmission. Empirical evidence shows that the semantic quality metric ranges from 90% to 100% for images containing fewer objects and 80% to 98% for those with more objects. In comparison, an autoencoder-based communication system exhibits a range of 80% to 100% for simpler images and 75% to 95% for more complex ones. These findings highlight the robustness of our proposed metric across different semantic communication frameworks, contributing to the advancement of semantic information transmission and setting a foundation for future research in this field.