Image Compression System with Privacy in Intent-based Healthcare Networking

Jing Wang, Jianhui Lv, Di Cui

Abstract: The emergence of Intent-based Networking(IBN)-enabled Healthcare Internet of Things (H-IoT) environments brings new challenges and opportunities for deploying intelligent medical image compression systems in real-time and privacy-sensitive scenarios. However, most existing medical image compression approaches are manually designed and optimized without accounting for the high dimensionality of medical data and the strict deployment constraints in heterogeneous IBN environments, resulting in suboptimal performance and limited adaptability. To address these challenges, we propose a novel implicit neural representation (INR)-based framework for automated medical image compression, leveraging the powerful continuous signal modeling capabilities of INRs to achieve high fidelity on high-dimensional medical images while maintaining compactness and adaptability. Our framework integrates an evolutionary architecture search strategy with privacy-constrained optimization and parameter quantization, enabling the architectures that balance reconstruction quality, latency, communication cost, and privacy. To validate the effectiveness of the framework, we design and conduct comprehensive simulation experiments in realistic multi-hospital IBN environments. The results demonstrate that our INR-based designs outperform traditional and data-driven baselines in both objective metrics and deployment feasibility under constrained resources.

External IDs:doi:10.1109/jiot.2025.3633641