We introduce a modality-agnostic neural compression algorithm based on a functional view of data and parameterised as an Implicit Neural Representation (INR). Bridging the gap between latent coding and sparsity, we obtain compact latent representations non-linearly mapped to a soft gating mechanism. This allows the specialisation of a shared INR network to each data item through subnetwork selection. After obtaining a dataset of such latent representations, we directly optimise the rate/distortion trade-off in a modality-agnostic space using neural compression. Variational Compression of Implicit Neural Representations (VC-INR) shows improved performance given the same representational capacity pre quantisation while also outperforming previous quantisation schemes used for other INR techniques. Our experiments demonstrate strong results over a large set of diverse modalities using the same algorithm without any modality-specific inductive biases. We show results on images, climate data, 3D shapes and scenes as well as audio and video, introducing VC-INR as the first INR-based method to outperform codecs as well-known and diverse as JPEG 2000, MP3 and AVC/HEVC on their respective modalities.

翻译：我们提出了一种基于数据函数视角且参数化为隐式神经表示（INR）的模态无关神经压缩算法。通过弥合潜在编码与稀疏性之间的差距，我们获得了非线性映射至软门控机制的紧凑潜在表示。这使得共享INR网络能够通过子网络选择针对每个数据项实现专门化。在获得此类潜在表示数据集后，我们利用神经压缩直接在模态无关空间中对率失真权衡进行优化。隐式神经表示的变分压缩（VC-INR）在量化前相同表示能力下展现出更优性能，同时超越了其他INR技术中使用的先前量化方案。实验表明，该算法无需任何特定模态的归纳偏置，即可在多种模态下取得优异结果。我们在图像、气候数据、三维形状与场景以及音频和视频上展示了结果，将VC-INR引入为首个在各自模态上性能超越JPEG 2000、MP3和AVC/HEVC等知名多样化编解码器的基于INR的方法。