CoINR: Compressed Implicit Neural Representations

Dhananjaya Jayasundara, Sudarshan Rajagopalan, Don Yasiru Lakshan Ranasinghe, Trac Tran, Vishal M. Patel

Keywords: Implicit Neural Representations, Signal Compression, Model Compression, Compressed Sensing

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 CoINR, 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 CoINR achieves substantial reductions in storage requirements for INRs across various configurations, outperforming conventional INR-based compression baselines. Furthermore, CoINR maintains high-quality decoding across diverse data modalities, including images, occupancy fields, and Neural Radiance Fields.

Primary Area: applications to computer vision, audio, language, and other modalities

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Submission Number: 7902