  Recently Implicit Neural Representations (INRs) gained attention as a novel
and effective representation for various data types. Thus far, prior work
mostly focused on optimizing their reconstruction performance. This work
investigates INRs from a novel perspective, i.e., as a tool for image
compression. To this end, we propose the first comprehensive compression
pipeline based on INRs including quantization, quantization-aware retraining
and entropy coding. Encoding with INRs, i.e. overfitting to a data sample, is
typically orders of magnitude slower. To mitigate this drawback, we leverage
meta-learned initializations based on MAML to reach the encoding in fewer
gradient updates which also generally improves rate-distortion performance of
INRs. We find that our approach to source compression with INRs vastly
outperforms similar prior work, is competitive with common compression
algorithms designed specifically for images and closes the gap to
state-of-the-art learned approaches based on Rate-Distortion Autoencoders.
Moreover, we provide an extensive ablation study on the importance of
individual components of our method which we hope facilitates future research
on this novel approach to image compression.
