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
state-of-the-art neural compression solution, neurcomp, however, 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 that improves upon neurcomp to handle
large-scale time-varying datasets. At the heart of our approach is a multiscale
structure that uses the Laplacian pyramid for adaptive signal fitting via
implicit neural representation. 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. A deep compression
strategy is then employed to compact the resulting model. We demonstrate the
effectiveness of ECNR with multiple datasets and compare it with neurcomp and
two state-of-the-art conventional compression methods (SZ3 and TTHRESH). Our
results position ECNR as a promising alternative to neurcomp for scientific
data compression.

本文介绍了一种高效的压缩神经表示（ECNR）解决方案，通过多尺度结构和深度压缩策略，改进了 neurcomp 以处理大规模时变数据集，结果表明 ECNR 是一种有前景的科学数据压缩替代方法。