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

翻译：本文提出了一种基于量化蒸馏低秩神经辐射场（QDLR-NeRF）的新型光场压缩方法。现有压缩方法直接编码光场子孔径图像集，而本文方法通过神经辐射场（NeRF）学习隐式场景表示，同时支持视图合成。为减小模型规模，首先在交替方向乘子法（ADMM）优化框架下，利用张量列（TT）分解施加低秩（LR）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模型优化中同时考虑低秩约束与率约束权重量化存在挑战。为解决该问题，我们引入网络蒸馏操作，将低秩近似与权重量化分阶段处理：基于LR-NeRF的TT分解，将初始低秩约束NeRF（LR-NeRF）的信息蒸馏至维度更小的模型（DLR-NeRF）；随后学习全局优化码本对所有TT分量进行量化，最终生成QDLR-NeRF。实验结果表明，与现有最优方法相比，本方法不仅实现了更优的压缩效率，还具备高质量合成任意光场视图的额外优势。