Implicit Neural Representations (INRs) and Neural Fields are a novel paradigm
for signal representation, from images and audio to 3D scenes and videos. The
fundamental idea is to represent a signal as a continuous and differentiable
neural network. This idea offers unprecedented benefits such as continuous
resolution and memory efficiency, enabling new compression techniques. However,
representing data as neural networks poses new challenges. For instance, given
a 2D image as a neural network, how can we further compress such a neural
image?. In this work, we present a novel analysis on compressing neural fields,
with the focus on images. We also introduce Adaptive Neural Images (ANI), an
efficient neural representation that enables adaptation to different inference
or transmission requirements. Our proposed method allows to reduce the
bits-per-pixel (bpp) of the neural image by 4x, without losing sensitive
details or harming fidelity. We achieve this thanks to our successful
implementation of 4-bit neural representations. Our work offers a new framework
for developing compressed neural fields.

该研究论文以压缩神经场为中心，介绍了自适应神经图像（ANI）的方法，能够在不损失细节和保持保真度的情况下将神经图像的每个像素位数降低了 4 倍，为发展压缩神经场提供了一个新框架。