LiDAR Depth Map Guided Image Compression Model
Abstract: 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.
- Pinliang Dong and Qi Chen, LiDAR remote sensing and applications, CRC Press, 2017.
- “Apple unveils new ipad pro with breakthrough lidar scanner and brings trackpad support to ipados,” https://www.apple.com/newsroom/2020/03/apple-unveils-new-ipad-pro-with-lidar-scanner-and-trackpad-support-in-ipados/, [Online; accessed 07-November-2023].
- “End-to-end optimized image compression,” arXiv preprint arXiv:1611.01704, 2016.
- “Variational image compression with a scale hyperprior,” arXiv preprint arXiv:1802.01436, 2018.
- “Variable rate deep image compression with a conditional autoencoder,” in Proc. of the IEEE/CVF ICCV, 2019, pp. 3146–3154.
- “End-to-end learnt image compression via non-local attention optimization and improved context modeling,” IEEE Transactions on Image Processing, vol. 30, pp. 3179–3191, 2021.
- “Conditional probability models for deep image compression,” in Proc. of the IEEE Conference on CVPR, 2018, pp. 4394–4402.
- “High-fidelity generative image compression,” Advances in Neural Inf. Proc. Syst., vol. 33, pp. 11913–11924, 2020.
- “Channel-wise autoregressive entropy models for learned image compression,” in 2020 IEEE ICIP. IEEE, 2020, pp. 3339–3343.
- JPEG: Still image data compression standard, Springer Science & Business Media, 1992.
- “The jpeg 2000 still image compression standard,” IEEE Signal processing magazine, vol. 18, no. 5, pp. 36–58, 2001.
- F Bellard, “Bpg image format.,” 2014.
- “Nonlinear transform coding,” IEEE Journal of Selected Topics in Signal Processing, vol. 15, no. 2, pp. 339–353, 2020.
- Lee D Davisson, “Rate-distortion theory and application,” Proc. of the IEEE, vol. 60, no. 7, pp. 800–808, 1972.
- “Transformer-based variable-rate image compression with region-of-interest control,” arXiv preprint arXiv:2305.10807, 2023.
- “Generative adversarial networks for extreme learned image compression,” in Proc. of the IEEE/CVF ICCV, 2019, pp. 221–231.
- T. Berger, Rate Distortion Theory: A Mathematical Basis For Data Compression, Englewood Cliffs, NJ: Prentice-Hall, 1971.
- “Transformer-based image compression,” in 2022 Data Compression Conference (DCC). IEEE, 2022, pp. 469–469.
- “Transformer-based image compression,” arXiv preprint arXiv:2111.06707, 2021.
- “Visual prompt tuning,” in ECCV. Springer, 2022, pp. 709–727.
- “Arkitscenes: A diverse real-world dataset for 3d indoor scene understanding using mobile rgb-d data,” arXiv preprint arXiv:2111.08897, 2021.
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