Volumetric Image Compression

Volumetric image compression aims to efficiently store and transmit large 3D datasets, crucial for fields like medicine and video gaming. Current research focuses on improving compression ratios and speed through novel neural network architectures, including those based on neural radiance fields (NeRFs), recurrent convolutional networks, and learned wavelet transforms, often incorporating multiscale or adaptive strategies. These advancements leverage deep learning to better capture and exploit inherent data redundancies, leading to significant improvements over traditional methods in both lossy and lossless compression. The resulting efficiency gains are vital for handling the ever-increasing volume of 3D data generated across various scientific and commercial applications.