  Light field technology has increasingly attracted the attention of the
research community with its many possible applications. The lenslet array in
commercial plenoptic cameras helps capture both the spatial and angular
information of light rays in a single exposure. While the resulting high
dimensionality of light field data enables its superior capabilities, it also
impedes its extensive adoption. Hence, there is a compelling need for efficient
compression of light field images. Existing solutions are commonly composed of
several separate modules, some of which may not have been designed for the
specific structure and quality of light field data. This increases the
complexity of the codec and results in impractical decoding runtimes. We
propose a new learning-based, disparity-aided model for compression of 4D light
field images capable of parallel decoding. The model is end-to-end trainable,
eliminating the need for hand-tuning separate modules and allowing joint
learning of rate and distortion. The disparity-aided approach ensures the
structural integrity of the reconstructed light fields. Comparisons with the
state of the art show encouraging performance in terms of PSNR and MS-SSIM
metrics. Also, there is a notable gain in the encoding and decoding runtimes.
Source code is available at https://moha23.github.io/LF-DAAE.
