  An unfocused 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).
