Lightweight Compression Networks for Efficient RGBD-IR Imagery Transmission on Resource-Constrained Edge UAV Platforms

Due to the limitation of unstable wireless links and resource-constrained edge computing device, it is challenging to transmit the unmanned aerial vehicles (UAVs) captured images with large data volume in real-time. To address this issue, this paper proposes a lightweight hybrid compression method named Jpeg Plus Plus Network (JppNet) for efficient multimodal image transmission. Different from existing methods that mainly focus on RGB image compression for offline storage and often use large networks for coding and reconstruction, the proposed JppNet focuses more on edge deployment under limited computing resources. At the encoder side, JPEG is first used to perform high-ratio compression on the RGB, D, and IR channels. Then, a lightweight convolutional encoder network extracts supplementary binary features for progressive transmission. At the decoder side, a medium-scale reconstruction network fuses the supplementary code with the JPEG reconstruction to restore image details. To verify the effectiveness of the proposed method, this paper built a multimodal data acquisition prototype and collected 20,000 high-quality RGBD-IR image samples for training and testing. Experimental results show that JppNet combines the high coding efficiency of JPEG with the detail recovery ability of convolutional neural networks. With low computational cost, JppNet obtains much lower reconstruction errors than JPEG on the RGB, D, and IR channels, especially at very low compression ratios. This supports the efficient transmission of UAV captured multimodal images under limited communication conditions.