Toward neural light-field compression

We propose a data compression method for a light field using a compact and computationally efficient neural representation. We first train a neural network with learnable parameters to reproduce the target light field. We then compress the set of learned parameters as an alternative representation of the light field. Our method is significantly different in concept from the traditional approaches where a light field is encoded as a set of images or a video (as a pseudo-temporal sequence) using off-the-shelf image/video codecs. We experimentally show that our method achieves a promising rate-distortion performance.