Recently, there has been extensive progress in developing autostereoscopic platforms for display purposes to present real-world 3D scenes. Light fields are the best emerging choice for computational multi-view autostereoscopic displays since they provide an optimized solution to support direction-dependent outputs simultaneously without sacrificing the resolution. We present a novel light field representation, coding and streaming scheme that efficiently handles large tensor data. Intrinsic redundancies in light field subsets are eliminated through low-rank representation using Tucker decomposition with tensor sketching for various ranks and sketch dimension parameters, making it ideal for streaming and transmission. Apart from removing spatial redundancies, the approximated light field is used to construct a Fourier disparity layers representation to further exploit other non-linear, temporal, intra and inter-view correlations present among the approximated sub-aperture images. Four scanning or view prediction patterns are utilized and the subsets in each pattern hierarchically construct the FDL representation and synthesize subsequent views. Iterative refinement and encoding with HEVC are followed by the final light field reconstruction. The complete end-to-end processing pipeline can flexibly work for multiple bitrates and is adaptable for a variety of multi-view autostereoscopic platforms. The compression performance of the proposed scheme is analyzed on real light fields. We achieved substantial bitrate savings compared to state-of-the-art codecs, while maintaining good reconstruction quality.
