Image Compression Using Novel View Synthesis Priors
This addresses bandwidth limitations in underwater acoustic communication for tetherless vehicle operations, though it appears incremental as it builds on existing novel view synthesis methods.
The paper tackles the problem of real-time image transmission for underwater remotely operated vehicles by proposing a model-based compression technique that uses novel view synthesis priors and gradient descent optimization to refine latent representations, achieving superior compression ratios and image quality over existing methods on an artificial ocean basin dataset.
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.