  Common representations of light fields use four-dimensional data structures,
where a given pixel is closely related not only to its spatial neighbours
within the same view, but also to its angular neighbours, co-located in
adjacent views. Such structure presents increased redundancy between pixels,
when compared with regular single-view images. Then, these redundancies are
exploited to obtain compressed representations of the light field, using
prediction algorithms specifically tailored to estimate pixel values based on
both spatial and angular references. This paper proposes new encoding schemes
which take advantage of the four-dimensional light field data structures to
improve the coding performance of Minimum Rate Predictors. The proposed methods
expand previous research on lossless coding beyond the current
state-of-the-art. The experimental results, obtained using both traditional
datasets and others more challenging, show bit-rate savings no smaller than
10%, when compared with existing methods for lossless light field compression.
