SPIFF: Selective Preservation of Image Fidelity for Bandwidth-constrained Heterogeneous Networks

Transmitting rich visual data in resource- constrained environments like Non-Terrestrial Networks (NTNs) poses a significant challenge. While current Semantic Communication (SC) approaches reduce bandwidth consumption, they often lack flexibility and/or compromise the perceptual fidelity of critical details. This paper first analyzes the fundamental trade-offs that exist between perceptual fidelity, semantic fidelity, and bandwidth utilization. It then introduces SPIFF, an SC-Generative AI framework that, by supporting selective fidelity, enables fine-grained control over the above trade-offs while meeting delay requirements. SPIFF features a lightweight, semantic-aware encoder performing semantic segmentation and applying a novel patch preservation strategy that retains perceptually significant regions while adapting lower-relevance areas compression to bandwidth availability. SPIFF also offloads high-complexity reconstruction tasks to a Generative AI-enabled decoder at the receiver, thus addressing asymmetric computation requirements. To support adaptation under dynamic conditions, while meeting system and application constraints, we equip SPIFF with a learning-based decision engine that is able to cope with the system non-linearities and effectively tune SPIFF’s configuration online. We evaluate SPIFF by implementing a full encoder-decoder pipeline. Results show that SPIFF fulfills perceptual reconstruction quality in scenarios where SC fails, and improves over state-of-the-art solutions both bandwidth savings (by up to 21%) and perceptual fidelity (by up to 13%).