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、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到软门控机制的紧凑潜在表示。这使得每个数据项能够通过子网络选择实现共享INR网络的专门化。在获得此类潜在表示的数据集后，我们在模态无关空间中直接使用神经压缩优化率失真权衡。隐式神经表示的变分压缩（VC-INR）在量化前给定相同表示容量的情况下展现出改进的性能，同时超越了其他INR技术中使用的先前量化方案。我们的实验表明，该算法在大量不同模态上无需任何模态特定归纳偏置即可获得强劲结果。我们在图像、气候数据、3D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超越如JPEG 2000、MP3和AVC/HEVC等知名且多样编解码器的基于INR方法。