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.

中文翻译：

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改进的深度分布式光场编码


光场通过捕获比传统 2D 成像更高维度的场景，能够提高视觉体验的真实度和沉浸感。另一方面，与传统视频相比，更高的维度需要大量的存储和传输开销。传统的编码方案通过采用非对称编解码器设计来实现高编码增益，其中编码器比解码器复杂得多。然而，在光场的情况下，不同相机之间的通信和处理可能会很昂贵，并且在编码器和解码器之间交换复杂性的可能性成为理想的特征。我们利用分布式源编码范例来有效降低编码器的复杂性，但代价是增加解码器端的计算量。具体来说，我们训练两个深度神经网络来改进分布式源编码方案的两个最关键的部分：辅助信息的预测和预测中不确定性的估计。实验表明 BD 速率提升相当可观，比 HEVC-Intra 提高了 59% 以上，比我们之前的方法 DLFC-I 提高了 17.45%。