Due to its conceptual simplicity and generality, compressive neural representation has emerged as a promising alternative to traditional compression methods for managing massive volumetric datasets. The current practice of neural compression utilizes a single large multilayer perceptron (MLP) to encode the global volume, incurring slow training and inference. This paper presents an efficient compressive neural representation (ECNR) solution for time-varying data compression, utilizing the Laplacian pyramid for adaptive signal fitting. Following a multiscale structure, we leverage multiple small MLPs at each scale for fitting local content or residual blocks. By assigning similar blocks to the same MLP via size uniformization, we enable balanced parallelization among MLPs to significantly speed up training and inference. Working in concert with the multiscale structure, we tailor a deep compression strategy to compact the resulting model. We show the effectiveness of ECNR with multiple datasets and compare it with state-of-the-art compression methods (mainly SZ3, TTHRESH, and neurcomp). The results position ECNR as a promising solution for volumetric data compression.

翻译：[translated abstract in Chinese] 由于其概念简洁性和通用性，压缩神经表示已成为管理大规模体数据时传统压缩方法的一种有前途替代方案。当前的神经压缩实践采用单一大型多层感知器对全局体数据进行编码，导致训练和推理速度缓慢。本文提出了一种针对时变数据压缩的高效压缩神经表示方法，利用拉普拉斯金字塔进行自适应信号拟合。我们采用多尺度结构，在每个尺度上利用多个小型多层感知器来拟合局部内容或残差块。通过尺寸均匀化将相似块分配给相同的多层感知器，我们实现了多层感知器间的均衡并行化，从而显著加速训练和推理过程。结合多尺度结构，我们定制了一种深度压缩策略来精简最终模型。我们通过多个数据集展示了ECNR的有效性，并将其与当前最先进的压缩方法（主要是SZ3、TTHRESH和neurcomp）进行了比较。结果表明ECNR是体数据压缩的一种有前景的解决方案。