View Synthesis-based Distributed Light Field Compression

Light field imaging is becoming a key technology, which provides users with a realistic visual experience through the capability of dynamic viewpoint shifting. This ability comes at the cost of capturing huge amounts of information, leaving the problem of compression and transmission a challenge. The encoder complexity is the key to achieve efficient coding in conventional light field coding schemes, where a complicated prediction process is essentially used at the encoder side to exploit the redundancy present in the light field image. We employ Distributed Source Coding (DSC) for light field images, which can extensively lift the computational requirement from the encoding side at the expense of increased computational complexity at the decoder side. The efficiency of DSC is heavily dependent on the quality of side information at the decoder. Therefore, we propose to leverage a learning-based view synthesis method, which takes into account the light field structure to generate high-quality side information. We compare our approach to Distributed Video Coding and Distributed Multi-view Video Coding schemes adapted to the light field framework and relevant standard-based approach, and demonstrate that the proposed view synthesis-based approach can achieve similar performance, while substantially reducing the number of key views to be transmitted.