  Neural compression algorithms are typically based on autoencoders that
require specialized encoder and decoder architectures for different data
modalities. In this paper, we propose COIN++, a neural compression framework
that seamlessly handles a wide range of data modalities. Our approach is based
on converting data to implicit neural representations, i.e. neural functions
that map coordinates (such as pixel locations) to features (such as RGB
values). Then, instead of storing the weights of the implicit neural
representation directly, we store modulations applied to a meta-learned base
network as a compressed code for the data. We further quantize and entropy code
these modulations, leading to large compression gains while reducing encoding
time by two orders of magnitude compared to baselines. We empirically
demonstrate the feasibility of our method by compressing various data
modalities, from images and audio to medical and climate data.
