LIGHT FIELD IMAGE COMPRESSION BASED ON DEEP LEARNING

In this paper, we propose a novel light field image compression scheme by exploiting the intrinsic similarity of light field images with deep learning. In particular, instead of conveying all LF sub-views, only sparsely sampled LF sub-views are compressed and the remaining sub-views are reconstructed from the coded sub-views in the neighbourhood with convolutional neural network (CNN). To jointly suppress the artifacts induced in compression and reconstruct the un-coded views with high geometric accuracy, a multi-view joint enhancement network is introduced to improve the coding performance. Extensive experiments show the superior compression performance of our scheme compared with the state-of-the-art methods.