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This paper presents a novel hierarchical coding scheme for light fields basedon transmittance patterns of low-rank multiplicative layers and Fourierdisparity layers. The proposed scheme identifies multiplicative layers of lightfield view subsets optimized using a convolutional neural network for differentscanning orders. Our approach exploits the hidden low-rank structure in themultiplicative layers obtained from the subsets of different scanning patterns.The spatial redundancies in the multiplicative layers can be efficientlyremoved by performing low-rank approximation at different ranks on the Krylovsubspace. The intra-view and inter-view redundancies between approximatedlayers are further removed by HEVC encoding. Next, a Fourier disparity layerrepresentation is constructed from the first subset of the approximated lightfield based on the chosen hierarchical order. Subsequent view subsets aresynthesized by modeling the Fourier disparity layers that iteratively refinethe representation with improved accuracy. The critical advantage of theproposed hybrid layered representation and coding scheme is that it utilizesnot just spatial and temporal redundancies in light fields but efficientlyexploits intrinsic similarities among neighboring sub-aperture images in bothhorizontal and vertical directions as specified by different predicationorders. In addition, the scheme is flexible to realize a range of multiplebitrates at the decoder within a single integrated system. The compressionperformance of the proposed scheme is analyzed on real light fields. Weachieved substantial bitrate savings and maintained good light fieldreconstruction quality.

A Novel Hierarchical Light Field Coding Scheme Based on Hybrid Stacked  Multiplicative Layers and Fourier Disparity Layers for Glasses-Free 3D  Displays