Learning Kernel-Modulated Neural Representation for Efficient Light Field Compression

Light fields capture 3D scene information by recording light rays emitted from a scene at various orientations. They offer a more immersive perception, compared with classic 2D images, but at the cost of huge data volumes. In this paper, we design a compact neural network representation for the light field compression task. In the same vein as the deep image prior, the neural network takes randomly initialized noise as input and is trained in a supervised manner in order to best reconstruct the target light field Sub-Aperture Images (SAIs). The network is composed of two types of complementary kernels: descriptive kernels (descriptors) that store scene description information learned during training, and modulatory kernels (modulators) that control the rendering of different SAIs from the queried perspectives. To further enhance compactness of the network meanwhile retain high quality of the decoded light field, we propose modulator allocation and apply kernel tensor decomposition techniques, followed by non-uniform quantization and lossless entropy coding. Extensive experiments demonstrate that our method outperforms other state-of-the-art (SOTA) methods by a significant margin in the light field compression task. Moreover, after adapting descriptors, the modulators learned from one light field can be transferred to new light fields for rendering dense views, showing the potential of the solution for view synthesis.