Efficient Semantic Image Communication for Traffic Monitoring at the Edge

2026-04-14

AI Summary

This work addresses the tension between bandwidth constraints and semantic fidelity in edge-based traffic monitoring by proposing two semantic image communication schemes, MMSD and SAMR. Both adopt an asymmetric architecture featuring lightweight edge-side processing and server-side generative reconstruction. MMSD replaces raw pixels with segmentation maps, edge maps, and textual descriptions, achieving a 99% compression ratio and high confidentiality. SAMR employs a semantic importance mask to guide a diffusion model for generative inpainting, preserving superior visual quality at a 99.1% compression ratio. Implemented on a Raspberry Pi 5, the system enables real-time operation—approximately 15 seconds for MMSD and 9 seconds for SAMR—significantly outperforming baseline methods such as SPIC, JPEG, and SQ-GAN.

Technology Category

• Machine Learning: Learning on the Edge & Model Compression
• Computer Vision: Multi-modal Vision
• Multiagent Systems: Agent Communication

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Cloud, edge and content delivery systems for the Web
• Security and Privacy: Large-scale security measurements
• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines

Abstract

Many visual monitoring systems operate under strict communication constraints, where transmitting full-resolution images is impractical and often unnecessary. In such settings, visual data is often used for object presence, spatial relationships, and scene context rather than exact pixel fidelity. This paper presents two semantic image communication pipelines for traffic monitoring, MMSD and SAMR, that reduce transmission cost while preserving meaningful visual information. MMSD (Multi-Modal Semantic Decomposition) targets very high compression together with data confidentiality, since sensitive pixel content is not transmitted. It replaces the original image with compact semantic representations, namely segmentation maps, edge maps, and textual descriptions, and reconstructs the scene at the receiver using a diffusion-based generative model. SAMR (Semantic-Aware Masking Reconstruction) targets higher visual quality while maintaining strong compression. It selectively suppresses non-critical image regions according to semantic importance before standard JPEG encoding and restores the missing content at the receiver through generative inpainting. Both designs follow an asymmetric sender-receiver architecture, where lightweight processing is performed at the edge and computationally intensive reconstruction is offloaded to the server. On a Raspberry Pi~5, the edge-side processing time is about 15s for MMSD and 9s for SAMR. Experimental results show average transmitted-data reductions of 99% for MMSD and 99.1% for SAMR. In addition, MMSD achieves lower payload size than the recent SPIC baseline while preserving strong semantic consistency, whereas SAMR provides a better quality-compression trade-off than standard JPEG and SQ-GAN under comparable operating conditions.

Problem

• semantic image communication
• traffic monitoring
• edge computing
• data compression
• communication constraints

Innovation

• semantic image communication
• edge computing
• generative reconstruction
• asymmetric architecture
• high compression