  A plenoptic light field (LF) camera places an array of microlenses in front
of an image sensor in order to separately capture different directional rays
arriving at an image pixel. Using a conventional Bayer pattern, data captured
at each pixel is a single color component (R, G or B). The sensed data then
undergoes demosaicking (interpolation of RGB components per pixel) and
conversion to an array of sub-aperture images (SAIs). In this paper, we propose
a new LF image coding scheme based on graph lifting transform (GLT), where the
acquired sensor data are coded in the original captured form without
pre-processing. Specifically, we directly map raw sensed color data to the
SAIs, resulting in sparsely distributed color pixels on 2D grids, and perform
demosaicking at the receiver after decoding. To exploit spatial correlation
among the sparse pixels, we propose a novel intra-prediction scheme, where the
prediction kernel is determined according to the local gradient estimated from
already coded neighboring pixel blocks. We then connect the pixels by forming a
graph, modeling the prediction residuals statistically as a Gaussian Markov
Random Field (GMRF). The optimal edge weights are computed via a graph learning
method using a set of training SAIs. The residual data is encoded via
low-complexity GLT. Experiments show that at high PSNRs -- important for
archiving and instant storage scenarios -- our method outperformed
significantly a conventional light field image coding scheme with demosaicking
followed by High Efficiency Video Coding (HEVC).
