We propose in this paper a Quantized Distilled Low-Rank Neural Radiance Field (QDLR-NeRF) representation for the task of light field compression. While existing compression methods encode the set of light field sub-aperture images, our proposed method learns an implicit scene representation in the form of a Neural Radiance Field (NeRF), which also enables view synthesis. To reduce its size, the model is first learned under a Low-Rank (LR) constraint using a Tensor Train (TT) decomposition within an Alternating Direction Method of Multipliers (ADMM) optimization framework. To further reduce the model's size, the components of the tensor train decomposition need to be quantized. However, simultaneously considering the optimization of the NeRF model with both the low-rank constraint and rate-constrained weight quantization is challenging. To address this difficulty, we introduce a network distillation operation that separates the low-rank approximation and the weight quantization during network training. The information from the initial LR-constrained NeRF (LR-NeRF) is distilled into a model of much smaller dimension (DLR-NeRF) based on the TT decomposition of the LR-NeRF. We then learn an optimized global codebook to quantize all TT components, producing the final QDLR-NeRF. Experimental results show that our proposed method yields better compression efficiency compared to state-of-the-art methods, and it additionally has the advantage of allowing the synthesis of any light field view with high quality.

翻译：本文提出一种量化蒸馏低秩神经辐射场（QDLR-NeRF）表示方法以完成光场压缩任务。现有压缩方法直接编码光场子孔径图像集合，而本方法以神经辐射场（NeRF）形式学习隐式场景表示，同时具备视角合成能力。为减小模型规模，首先在交替方向乘子法（ADMM）优化框架下，通过张量列（TT）分解施加低秩约束进行模型学习。为进一步压缩模型尺寸，需对张量列分解的各分量进行量化。然而，在NeRF模型中同时优化低秩约束与率约束权重量化存在挑战。为解决该难题，我们引入网络蒸馏操作，在训练过程中将低秩近似与权重量化分离。基于初始低秩约束NeRF（LR-NeRF）的TT分解，将其信息蒸馏至维度更小的模型（DLR-NeRF）中。随后学习优化全局码本以量化所有TT分量，最终生成QDLR-NeRF。实验结果表明，与现有最优方法相比，本方法具有更优的压缩效率，并且额外具备高质量合成任意光场视角的能力。