With the recent widespread interest for head-mounted displays applied to virtual or augmented reality, holography has been considered as an appealing technique for a revolutionary and natural 3D visualization system. However, due to the tremendous amount of data required by holograms and to the very different properties of holographic data compared to common imagery, compression of digital holograms is a highly challenging topic for researchers. In this study, we introduce a novel approach, to the best of our knowledge, for color hologram compression based on matching pursuit using an overcomplete Gabor's dictionary. A detailed framework, together with a GPU implementation, from hologram decomposition to bitstream generation, is studied, and the results are discussed and compared to existing hologram compression algorithms.
Over the last few years, holography has been emerging as an alternative to stereoscopic imaging since it provides users with the most realistic and comfortable three-dimensional (3D) experience. However, high quality holograms enabling a free-viewpoint visualization contain tremendous amount of data. Therefore, a user willing to access to a remote hologram repository would face high downloading time, even with high speed networks. To reduce transmission time, a joint viewpointquality scalable compression scheme is proposed. At the encoder side, the hologram is first decomposed into a sparse set of diffracted light rays using Matching Pursuit over a Gabor atoms dictionary. Then, the atoms corresponding to a given user's viewpoint are selected to form a sub-hologram. Finally, the pruned atoms are sorted and encoded according to their importance for the reconstructed view. The proposed approach allows a progressive decoding of the sub-hologram from the first received atom. Streaming simulations for a moving user reveal that our approach outperforms conventional scalable codecs such as scalable H.265 and enables a practical streaming with a better quality of experience.
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