  To enrich the functionalities of traditional cameras, light field cameras
record both the intensity and direction of light rays, so that images can be
rendered with user-defined camera parameters via computations. The added
capability and flexibility are gained at the cost of gathering typically more
than $100\times$ greater amount of information than conventional images. To
cope with this issue, several light field compression schemes have been
introduced. However, their ways of exploiting correlations of multidimensional
light field data are complex and are hence not suited for inexpensive light
field cameras. In this work, we propose a novel $\ell_\infty$-constrained
light-field image compression system that has a very low-complexity DPCM
encoder and a CNN-based deep decoder. Targeting high-fidelity reconstruction,
the CNN decoder capitalizes on the $\ell_\infty$-constraint and light field
properties to remove the compression artifacts and achieves significantly
better performance than existing state-of-the-art $\ell_2$-based light field
compression methods.
