Cross-Modal Thermal Image Compression Via RGB Side Information

High-resolution (HR) thermal imaging is essential in domains such as autonomous navigation and remote sensing, but its deployment is often limited by the cost of thermal sensors and the bandwidth required to transmit HR thermal data. This paper presents a cross-modal thermal image compression framework that leverages high-resolution RGB images as side information to enhance and efficiently encode low-resolution (LR) thermal inputs. Our method first applies a Guided Thermal Image Super-Resolution (GTISR) network to generate an initial HR thermal estimate from the LR thermal image and its aligned RGB counterpart. Rather than compressing the super-resolved output directly, we compute and encode only the residual using a learned image compression (LIC) model, while the LR thermal image is encoded separately using a standard codec such as Versatile Video Coding (VVC). Because the residual has substantially lower entropy, this two-stage approach significantly improves rate–distortion performance. Evaluated on the Perception Beyond the Visible Spectrum (PBVS) 2024 GTISR Challenge dataset, our framework achieves a 17.09% BD-rate reduction over a thermal-only baseline. These results demonstrate the benefit of integrating RGB side information into the compression pipeline and highlight the potential of cross-modal strategies for efficient thermal imaging under bandwidth constraints.