  The paper addresses the problem of energy compaction of dense 4D light fields
by designing geometry-aware local graph-based transforms. Local graphs are
constructed on super-rays that can be seen as a grouping of spatially and
geometry-dependent angularly correlated pixels. Both non separable and
separable transforms are considered. Despite the local support of limited size
defined by the super-rays, the Laplacian matrix of the non separable graph
remains of high dimension and its diagonalization to compute the transform
eigen vectors remains computationally expensive. To solve this problem, we then
perform the local spatio-angular transform in a separable manner. We show that
when the shape of corresponding super-pixels in the different views is not
isometric, the basis functions of the spatial transforms are not coherent,
resulting in decreased correlation between spatial transform coefficients. We
hence propose a novel transform optimization method that aims at preserving
angular correlation even when the shapes of the super-pixels are not isometric.
Experimental results show the benefit of the approach in terms of energy
compaction. A coding scheme is also described to assess the rate-distortion
perfomances of the proposed transforms and is compared to state of the art
encoders namely HEVC and JPEG Pleno VM 1.1.
