Diffusion based scalable semantic communication framework for image compression and transmission

Semantic communication for image transmission enhances efficiency by focusing on meaning rather than pixel accuracy. However, existing methods struggle to preserve semantic integrity due to challenges in identifying key content and contextual relationships. To address this, we propose a scalable semantic communication model that prioritizes object-based semantic extraction and dynamic weighting to enhance image representation. The framework detects and extracts objects within an image, assigning significance through a weight map that emphasizes key elements while de-emphasizing irrelevant details. The system dynamically adjusts weights in a weight map for scalable image transmission, adapting to network infrastructure constraints like bandwidth and latency. This ensures only the most relevant data is sent. The weighted semantic representation feeds into a diffusion-based reconstruction model, regenerating high-fidelity images at low bit rates. Experimental results show this approach significantly improves image representation while minimizing transmission overhead, scaling effectively with network conditions. The ability to adaptively control semantic granularity makes it ideal for real-world applications, paving the way for efficient, context-aware image transmission with improved resource utilization and semantic preservation.