Light Field Compression using Fourier Disparity Layers

Elian Dib 1 Mikaël Le Pendu 2 Christine Guillemot 1
1 Sirocco - Analysis representation, compression and communication of visual data
Inria Rennes – Bretagne Atlantique , IRISA-D5 - SIGNAUX ET IMAGES NUMÉRIQUES, ROBOTIQUE
Abstract : In this paper, we present a compression method for light fields based on the Fourier Disparity Layer representation. This light field representation consists in a set of layers that can be efficiently constructed in the Fourier domain from a sparse set of views, and then used to reconstruct intermediate viewpoints without requiring a disparity map. In the proposed compression scheme, a subset of light field views is encoded first and used to construct a Fourier Disparity Layer model from which a second subset of views is predicted. After encoding and decoding the residual of those predicted views, a larger set of decoded views is available, allowing us to refine the layer model in order to predict the next views with increased accuracy. The procedure is repeated until the complete set of light field views is encoded. Following this principle, we investigate in the paper different scanning orders of the light field views and analyse their respective efficiencies regarding the compression performance.
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Elian Dib, Mikaël Le Pendu, Christine Guillemot. Light Field Compression using Fourier Disparity Layers. ICIP 2019 - IEEE International Conference on Image Processing, Sep 2019, Taipei, Taiwan. pp.1-5, ⟨10.1109/ICIP.2019.8803756⟩. ⟨hal-02130487⟩

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