用新视角合成先验压缩图像，提升水下远程作业实时性。
Image Compression Using Novel View Synthesis Priors
- 利用训练好的新视角合成模型，优化隐变量以生成可压缩的图像差异。
- 在人工海洋池数据集上实现更高压缩比和更优图像质量。
- 对场景中新物体和后向散射等现实退化具有强鲁棒性，适合水下任务。
实时视觉反馈对无缆遥控车辆的远控操作至关重要，尤其在检测与操作任务中。尽管声学通信是中距离水下通信的首选，但其带宽有限，难以实现实时传输图像或视频。为此，我们提出一种基于模型的图像压缩技术，利用先前任务信息作为先验。该方法采用训练好的基于机器学习的新视角合成模型，通过梯度下降优化隐表示，以生成相机图像与渲染图像之间的可压缩差异。我们在人工海洋池数据集上评估了该压缩技术，结果表明其在压缩比和图像质量上优于现有方法。此外，该方法对场景中新物体的引入表现出鲁棒性，凸显其在推进无缆遥控车辆操作方面的潜力。
原文摘要 · Abstract (English)
Real-time visual feedback is essential for tetherless control of remotely operated vehicles, particularly during inspection and manipulation tasks. Though acoustic communication is the preferred choice for medium-range communication underwater, its limited bandwidth renders it impractical to transmit images or videos in real-time. To address this, we propose a model-based image compression technique that leverages prior mission information. Our approach employs trained machine-learning based novel view synthesis models, and uses gradient descent optimization to refine latent representations to help generate compressible differences between camera images and rendered images. We evaluate the proposed compression technique using a dataset from an artificial ocean basin, demonstrating superior compression ratios and image quality over existing techniques. Moreover, our method exhibits robustness to introduction of new objects within the scene, highlighting its potential for advancing tetherless remotely operated vehicle operations.
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