Light Field Compression With Graph Learning and Dictionary-Guided Sparse Coding

Light field (LF) data are widely used in the immersive representations of the 3D world. To record the light rays along with different directions, an LF requires much larger storage space and transmission bandwidth than a conventional 2D image with similar spatial dimension. In this paper, we propose a novel framework for light field image compression that leverages graph learning and dictionary learning to remove structural redundancies between different views. Specifically, to significantly reduce the bit-rates, only a few key views are sampled and encoded, whereas the remaining non-key views are reconstructed via the graph adjacency matrix learned from the angular patch. Furthermore, dictionary-guided sparse coding is developed to compress the graph adjacency matrices and reduce the coding overheads. To our best knowledge, this paper is the first to achieve compact representation of cross-view structural information via adaptive learning on graphs. Experimental results demonstrate that the proposed framework achieves better performance than the standardized HEVC-based codec.

Automated summary

This paper proposes a novel framework for light field image compression that utilizes graph learning and dictionary learning techniques to remove structural redundancies between different views. The framework achieves significant reduction in bit-rates by sampling and encoding only a few key views and reconstructing non-key views using the graph adjacency matrix learned from angular patches. Dictionary-guided sparse coding is used to compress the graph adjacency matrices and reduce coding overheads.