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LiDAR Depth Map Guided Image Compression Model

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arxiv 2401.06517 v3 pith:3A63E3Y2 submitted 2024-01-12 eess.IV

Alessandro Gnutti , Stefano Della Fiore , Mattia Savardi , Yi-Hsin Chen , Riccardo Leonardi , Wen-Hsiao Peng This is my paper · ORCID
classification eess.IV
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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.

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Cited by 1 Pith paper

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  1. Sparse Point Clouds Assisted Learned Image Compression

    cs.CV 2024-12 conditional novelty 6.0 of 10

    Projecting sparse LiDAR depth into predicted structural features and injecting them into learned image codecs consistently improves rate-distortion performance on KITTI and Waymo.

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