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We introduce a modality-agnostic neural compression algorithm based on afunctional view of data and parameterised as an Implicit Neural Representation(INR). Bridging the gap between latent coding and sparsity, we obtain compactlatent representations non-linearly mapped to a soft gating mechanism. Thisallows the specialisation of a shared INR network to each data item throughsubnetwork selection. After obtaining a dataset of such latent representations,we directly optimise the rate/distortion trade-off in a modality-agnostic spaceusing neural compression. Variational Compression of Implicit NeuralRepresentations (VC-INR) shows improved performance given the samerepresentational capacity pre quantisation while also outperforming previousquantisation schemes used for other INR techniques. Our experiments demonstratestrong results over a large set of diverse modalities using the same algorithmwithout any modality-specific inductive biases. We show results on images,climate data, 3D shapes and scenes as well as audio and video, introducingVC-INR as the first INR-based method to outperform codecs as well-known anddiverse as JPEG 2000, MP3 and AVC/HEVC on their respective modalities.

Modality-Agnostic Variational Compression of Implicit Neural Representations