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 learningbased 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.
Light fields enable increasing the degree of realism and immersion of visual experience by capturing a scene with a higher number of dimensions than conventional 2D imaging. On another side, higher dimensionality entails significant storage and transmission overhead compared to traditional video. Conventional coding schemes achieve high coding gains by employing an asymmetric codec design, where the encoder is significantly more complex than the decoder. However, in the case of light fields, the communication and processing among different cameras could be expensive, and the possibility of trading the complexity between the encoder and the decoder becomes a desirable feature. We leverage the distributed source coding paradigm to effectively reduce the encoder's complexity at the cost of increased computation at the decoder side. Specifically, we train two deep neural networks to improve the two most critical parts of a distributed source coding scheme: the prediction of side information and the estimation of the uncertainty in the prediction. Experiments show considerable BD-rate gains, above 59% over HEVC-Intra and 17.45% over our previous method DLFC-I.
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