  Traditional representations for light fields can be separated into two types:
explicit representation and implicit representation. Unlike explicit
representation that represents light fields as Sub-Aperture Images (SAIs) based
arrays or Micro-Images (MIs) based lenslet images, implicit representation
treats light fields as neural networks, which is inherently a continuous
representation in contrast to discrete explicit representation. However, at
present almost all the implicit representations for light fields utilize SAIs
to train an MLP to learn a pixel-wise mapping from 4D spatial-angular
coordinate to pixel colors, which is neither compact nor of low complexity.
Instead, in this paper we propose MiNL, a novel MI-wise implicit neural
representation for light fields that train an MLP + CNN to learn a mapping from
2D MI coordinates to MI colors. Given the micro-image's coordinate, MiNL
outputs the corresponding micro-image's RGB values. Light field encoding in
MiNL is just training a neural network to regress the micro-images and the
decoding process is a simple feedforward operation. Compared with common
pixel-wise implicit representation, MiNL is more compact and efficient that has
faster decoding speed (\textbf{$\times$80$\sim$180} speed-up) as well as better
visual quality (\textbf{1$\sim$4dB} PSNR improvement on average).
