  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 learns stacked multiplicative layers from
subsets of light field views determined from different scanning orders. The
multiplicative layers are optimized using a fast data-driven convolutional
neural network (CNN). The spatial correlation in layer patterns is exploited
with varying low ranks in factorization derived from singular value
decomposition on a Krylov subspace. Further, encoding with HEVC efficiently
removes intra-view and inter-view correlation in low-rank approximated layers.
The initial subset of approximated decoded views from multiplicative
representation is used to construct Fourier disparity layer (FDL)
representation. The FDL model synthesizes second subset of views which is
identified by a pre-defined hierarchical prediction order. The correlations
between the prediction residue of synthesized views is further eliminated by
encoding the residual signal. The set of views obtained from decoding the
residual is employed in order to refine the FDL model and predict the next
subset of views with improved accuracy. This hierarchical procedure is repeated
until all light field views are encoded. The critical advantage of proposed
hybrid layered representation and coding scheme is that it utilizes not just
spatial and temporal redundancies, but efficiently exploits the strong
intrinsic similarities among neighboring sub-aperture images in both horizontal
and vertical directions as specified by different predication orders. Besides,
the scheme is flexible to realize a range of multiple bitrates at the decoder
within a single integrated system. The compression performance analyzed with
real light field shows substantial bitrate savings, maintaining good
reconstruction quality.
