Low-complexity ℓ ∞ -compression of light field images with a deep-decompression stage

To enrich the functionalities of traditional cameras, light field cameras record both the intensity and direction of light rays, so that images can be rendered with user-defined camera parameters via computations. The added capability and flexibility are gained at the cost of gathering typically more than 100 perspectives of the same scene, resulting in large data volume. To cope with this issue, several light field compression schemes have been introduced. However, their ways of exploiting correlations of multidimensional light field data are complex and are hence not suited for cost-effective light field cameras. On the other hand, existing simpler compression schemes do not offer good compression performance. In this work, we propose a novel ℓ ∞ -constrained light-field image compression system that has a very low-complexity DPCM encoder and a CNN-based deep decoder enhancement. Targeting high-fidelity soft-decoding (restoration), the CNN decoding capitalizes on the ℓ ∞ -constraint, i.e. the maximum absolute error bound, and light field properties to remove the compression artifacts. Two different architectures for CNN decoder enhancement are proposed, one is based on 2D CNNs and optimized for fast inference, and another is based on 3D CNNs to maximize ℓ 2 restoration quality. The proposed networks achieve superior performance both in inference speed and restoration quality in comparison to state-of-the-art light field network architectures. In conjunction with ℓ ∞ -EPIC, the proposed architecture, while satisfying a well-defined ℓ ∞ constraint, outperforms existing state-of-the-art ℓ 2 -based light field compression methods.