  The incorporation of LiDAR technology into some high-end smartphones has
unlocked numerous possibilities across various applications, including
photography, image restoration, augmented reality, and more. In this paper, we
introduce a novel direction that harnesses LiDAR depth maps to enhance the
compression of the corresponding RGB camera images. To the best of our
knowledge, this represents the initial exploration in this particular research
direction. Specifically, we propose a Transformer-based learned image
compression system capable of achieving variable-rate compression using a
single model while utilizing the LiDAR depth map as supplementary information
for both the encoding and decoding processes. Experimental results demonstrate
that integrating LiDAR yields an average PSNR gain of 0.83 dB and an average
bitrate reduction of 16% as compared to its absence.
