  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.
