  This paper presents a novel hierarchical coding scheme for light fields based
on transmittance patterns of low-rank multiplicative layers and Fourier
disparity layers. The proposed scheme identifies multiplicative layers of light
field view subsets optimized using a convolutional neural network for different
scanning orders. Our approach exploits the hidden low-rank structure in the
multiplicative layers obtained from the subsets of different scanning patterns.
The spatial redundancies in the multiplicative layers can be efficiently
removed by performing low-rank approximation at different ranks on the Krylov
subspace. The intra-view and inter-view redundancies between approximated
layers are further removed by HEVC encoding. Next, a Fourier disparity layer
representation is constructed from the first subset of the approximated light
field based on the chosen hierarchical order. Subsequent view subsets are
synthesized by modeling the Fourier disparity layers that iteratively refine
the representation with improved accuracy. The critical advantage of the
proposed hybrid layered representation and coding scheme is that it utilizes
not just spatial and temporal redundancies in light fields but efficiently
exploits intrinsic similarities among neighboring sub-aperture images in both
horizontal and vertical directions as specified by different predication
orders. In addition, the scheme is flexible to realize a range of multiple
bitrates at the decoder within a single integrated system. The compression
performance of the proposed scheme is analyzed on real light fields. We
achieved substantial bitrate savings and maintained good light field
reconstruction quality.
