Mr. Jun Young Jeong Profile

Jun Young Jeong

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Proceedings Article | 2 May 2024 Paper

Compression method of NeRF model using NNC and VVC

Dong-Ha Kim, Jun Young Jeong, Gwangsoon Lee, Jae-Gon Kim

Proceedings Volume 13164, 131642V (2024) https://doi.org/10.1117/12.3019533

KEYWORDS: Performance modeling, Voxels, Data modeling, Quantization, Video compression, Education and training, 3D modeling, Neural networks, Image compression, Video

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The Moving Picture Expert Groups (MPEG) recognized that the Neural Radiance Field (NeRF) could serve as a novel image and video compression approach. Therefore, the MPEG established the ad-hoc group called Implicit Neural Visual Representation (INVR) and is currently exploring the potential standardization of 6 Degree of Freedom (6DoF) video compression using NeRF-based technologies. The INVR group is investigating the compression and rendering performance of various NeRF models, including the basic NeRF composed of simple Multi-Layer Perceptrons (MLPs) and the hybrid NeRF that utilizes voxel grids along with MLPs. In this paper, we propose compression methods of basic NeRF and hybrid NeRF using the existing standards, Neural Network Compression (NNC) and Versatile Video Coding (VVC), respectively. The proposed method for the basic NeRF compression utilizes NNC with a network-adaptive bit allocation method. Additionally, the proposed method for compressing the hybrid NeRF, TensoRF, involves transforming the tensor-planes that constitute TensoRF into feature maps and compress them using VVC. In the validation experiments based on the common test conditions (CTCs) defined by INVR, the proposed hybrid NeRF compression demonstrates significantly higher BPP-PSNR performance compared to the state-of-the art method, Vector Quantized Radiance Field (VQRF).

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