  Light field image becomes one of the most promising media types for immersive
video applications. In this paper, we propose a novel end-to-end
spatial-angular-decorrelated network (SADN) for high-efficiency light field
image compression. Different from the existing methods that exploit either
spatial or angular consistency in the light field image, SADN decouples the
angular and spatial information by dilation convolution and stride convolution
in spatial-angular interaction, and performs feature fusion to compress spatial
and angular information jointly. To train a stable and robust algorithm, a
large-scale dataset consisting of 7549 light field images is proposed and
built. The proposed method provides 2.137 times and 2.849 times higher
compression efficiency relative to H.266/VVC and H.265/HEVC inter coding,
respectively. It also outperforms the end-to-end image compression networks by
an average of 79.6% bitrate saving with much higher subjective quality and
light field consistency.
