Towards Lossless Implicit Neural Representation via Bit Plane Decomposition

Woo Kyoung Han1, Byeonghun Lee1, Hyunmin Cho1, Sunghoon Im2*, Kyong Hwan Jin1*
Korea University1, DGIST2
CVPR 2025
*Indicates Corresponding Authors
Code arXiv

Demo of our Lossless INR of representing 2D Image.

Overview of the proposed method. The upper bound on the number of parameters of INR grow proportionally to a bit-precision. We propose a bit-plane decomposition method, reducing the upper bound, enabling faster convergence, and ultimately achieving a lossless representation.

Overall process of our proposed method. We improve the performance of INR by lowering the upper bound of the number of parameters and achieve lossless neural representation.

Abstract

We quantify the upper bound on the size of the implicit neural representation (INR) model from a digital perspective. The upper bound of the model size increases exponentially as the required bit-precision increases. To this end, we present a bit-plane decomposition method that makes INR predict bit-planes, producing the same effect as reducing the upper bound of the model size. We validate our hypothesis that reducing the upper bound leads to faster convergence with constant model size. Our method achieves lossless representation in 2D image and audio fitting, even for high bit-depth signals, such as 16-bit, which was previously unachievable. We pioneered the presence of bit bias, which INR prioritizes as the most significant bit (MSB). We expand the application of the INR task to bit depth expansion, lossless image compression, and extreme network quantization.