Light Field Image Sparse Coding via CNN-Based EPI Super-Resolution

This paper proposes a novel light field (LF) image compression scheme by super resolving the epipolar plane image (EPI) via convolutional neural network (CNN). In the scheme, we first decompose the LF image into sub-aperture images (SAIs), and only one quarter of them are compressed on the encoding side to reduce the bitrate. On the decoding side, we use these selected SAIs to reconstruct the entire LF by taking advantage of the special structure of EPI. The low-resolution EPIs generated from the sparse SAIs are super resolved by using deep residual network and the output high-resolution EPIs are used to rebuild the dense SAIs. Experimental results show the superior performance of our scheme, which achieve 1.46 dB quality improvement and 35.85 percent bit rate reduction on average compared with the typical pseudo-sequence-based coding method.