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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 learns stacked multiplicative layers fromsubsets of light field views determined from different scanning orders. Themultiplicative layers are optimized using a fast data-driven convolutionalneural network (CNN). The spatial correlation in layer patterns is exploitedwith varying low ranks in factorization derived from singular valuedecomposition on a Krylov subspace. Further, encoding with HEVC efficientlyremoves intra-view and inter-view correlation in low-rank approximated layers.The initial subset of approximated decoded views from multiplicativerepresentation is used to construct Fourier disparity layer (FDL)representation. The FDL model synthesizes second subset of views which isidentified by a pre-defined hierarchical prediction order. The correlationsbetween the prediction residue of synthesized views is further eliminated byencoding the residual signal. The set of views obtained from decoding theresidual is employed in order to refine the FDL model and predict the nextsubset of views with improved accuracy. This hierarchical procedure is repeateduntil all light field views are encoded. The critical advantage of proposedhybrid layered representation and coding scheme is that it utilizes not justspatial and temporal redundancies, but efficiently exploits the strongintrinsic similarities among neighboring sub-aperture images in both horizontaland vertical directions as specified by different predication orders. Besides,the scheme is flexible to realize a range of multiple bitrates at the decoderwithin a single integrated system. The compression performance analyzed withreal light field shows substantial bitrate savings, maintaining goodreconstruction quality.

A Hierarchical Coding Scheme for Glasses-free 3D Displays Based on Scalable Hybrid Layered Representation of Real-World Light Fields