The vast amount of light field data taken by plenoptic camera poses a great challenge for compression. Researchers attempt to design various sub-aperture image (SAI) prediction algorithms to reduce the redundancy inside the light field. However, they ignore the fact that it may limit the performance of light field compression when selecting the reference SAIs without regard to the scene content. In this paper, we propose a novel reference SAIs selection algorithm for a hierarchical compression structure from the perspective of light field energy maximization. Specifically, we firstly apply a low-rank model to the feature domain of light field, in which the features are extracted by the difference of Gaussian. In this way, a few SAIs containing sufficient light field energy are selected as the references, and the light field hierarchical structure is accordingly constructed by these selected SAIs and the remainder SAIs. Then, for better prediction of remainder SAIs in the hierarchical structure, we propose a compressive sensing-based weight determination algorithm, which computes the weight parameters for each selected reference SAI in pursuit of minimizing the prediction errors. Experimental results demonstrate that the hierarchical light field compression structure with the proposed reference SAIs selection algorithm can achieve better compression performance, as well as superior refocusing and depth-of-field extending capabilities compared to state-of-the-art methods.

全光相机拍摄的大量光场数据对压缩提出了很大的挑战。研究人员试图设计各种亚孔径图像（SAI）预测算法来减少光场内部的冗余。然而，他们忽略了这样一个事实，即在选择参考 SAI 时可能会限制光场压缩的性能，而不考虑场景内容。在本文中，我们从光场能量最大化的角度提出了一种用于分层压缩结构的新型参考 SAI 选择算法。具体来说，我们首先将低秩模型应用于光场的特征域，其中通过高斯差分提取特征。这样就选择了一些包含足够光场能量的SAI作为参考，并且光场层次结构是由这些选定的SAI和其余的SAI相应地构建的。然后，为了更好地预测分层结构中的剩余 SAI，我们提出了一种基于压缩感知的权重确定算法，该算法计算每个选定参考 SAI 的权重参数，以最小化预测误差。实验结果表明，与最先进的方法相比，具有所提出的参考 SAI 选择算法的分层光场压缩结构可以实现更好的压缩性能，以及卓越的重聚焦和景深扩展能力。我们提出了一种基于压缩感知的权重确定算法，该算法计算每个选定参考 SAI 的权重参数，以最小化预测误差。实验结果表明，与最先进的方法相比，具有所提出的参考 SAI 选择算法的分层光场压缩结构可以实现更好的压缩性能，以及卓越的重聚焦和景深扩展能力。我们提出了一种基于压缩感知的权重确定算法，该算法计算每个选定参考 SAI 的权重参数，以最小化预测误差。实验结果表明，与最先进的方法相比，具有所提出的参考 SAI 选择算法的分层光场压缩结构可以实现更好的压缩性能，以及卓越的重聚焦和景深扩展能力。