  Light field (LF) imaging has gained significant attention due to its recent
success in 3-dimensional (3D) displaying and rendering as well as augmented and
virtual reality usage. Nonetheless, because of the two extra dimensions, LFs
are much larger than conventional images. We develop a JPEG-assisted
learning-based technique to reconstruct an LF from a JPEG bitstream with a bit
per pixel ratio of 0.0047 on average. For compression, we keep the LF's center
view and use JPEG compression with 50% quality. Our reconstruction pipeline
consists of a small JPEG enhancement network (JPEG-Hance), a depth estimation
network (Depth-Net), followed by view synthesizing by warping the enhanced
center view. Our pipeline is significantly faster than using video compression
on pseudo-sequences extracted from an LF, both in compression and
decompression, while maintaining effective performance. We show that with a 1%
compression time cost and 18x speedup for decompression, our methods
reconstructed LFs have better structural similarity index metric (SSIM) and
comparable peak signal-to-noise ratio (PSNR) compared to the state-of-the-art
video compression techniques used to compress LFs.
