LiDAR Depth Map Guided Image Compression Model

• DOI: 10.48550/arxiv.2401.06517
• Publication Date: 2024-10-27
• Authors: Alessandro Gnutti; Stefano Della Fiore; Mattia Savardi; Yi-Hsin Chen; Riccardo Leonardi; Wen-Hsiao Peng
• Keywords: Image and Video Processing (eess.IV); 0202 electrical engineering, electronic engineering, information engineering; FOS: Electrical engineering, electronic engineering, information engineering; 02 engineering and technology; Electrical Engineering and Systems Science - Image and Video Processing

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.