SINR: Sparsity Driven Compressed Implicit Neural Representations
Journal:
arXiv
Published Date:
Mar 25, 2025
Abstract
Implicit Neural Representations (INRs) are increasingly recognized as a
versatile data modality for representing discretized signals, offering benefits
such as infinite query resolution and reduced storage requirements. Existing
signal compression approaches for INRs typically employ one of two strategies:
1. direct quantization with entropy coding of the trained INR; 2. deriving a
latent code on top of the INR through a learnable transformation. Thus, their
performance is heavily dependent on the quantization and entropy coding schemes
employed. In this paper, we introduce SINR, an innovative compression algorithm
that leverages the patterns in the vector spaces formed by weights of INRs. We
compress these vector spaces using a high-dimensional sparse code within a
dictionary. Further analysis reveals that the atoms of the dictionary used to
generate the sparse code do not need to be learned or transmitted to
successfully recover the INR weights. We demonstrate that the proposed approach
can be integrated with any existing INR-based signal compression technique. Our
results indicate that SINR achieves substantial reductions in storage
requirements for INRs across various configurations, outperforming conventional
INR-based compression baselines. Furthermore, SINR maintains high-quality
decoding across diverse data modalities, including images, occupancy fields,
and Neural Radiance Fields.
