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.

翻译：隐式神经表示（INRs）和神经场是一种用于信号表示的新范式，涵盖从图像、音频到3D场景和视频的多种数据类型。其核心思想是将信号表示为连续且可微的神经网络。这一理念带来了前所未有的优势，例如连续分辨率和内存效率，从而催生了新的压缩技术。然而，将数据表示为神经网络也带来了新的挑战。例如，给定一个以神经网络形式表示的2D图像，我们应如何进一步压缩此类神经图像？在本研究中，我们提出了一种针对神经场压缩的新颖分析，并重点关注图像领域。同时，我们引入了自适应神经图像（ANI）——一种高效的神经表示方法，能够适应不同的推理或传输需求。我们提出的方法可将神经图像的每像素比特数（bpp）降低4倍，且不会丢失敏感细节或损害保真度。这一成果得益于我们成功实现的4位神经表示。我们的工作为开发压缩神经场提供了新的框架。