Light field technology has increasingly attracted the attention of the research community with its many possible applications. The lenslet array in commercial plenoptic cameras helps capture both the spatial and angular information of light rays in a single exposure. While the resulting high dimensionality of light field data enables its superior capabilities, it also impedes its extensive adoption. Hence, there is a compelling need for efficient compression of light field images. Existing solutions are commonly composed of several separate modules, some of which may not have been designed for the specific structure and quality of light field data. This increases the complexity of the codec and results in impractical decoding runtimes. We propose a new learning-based, disparity-aided model for compression of 4D light field images capable of parallel decoding. The model is end-to-end trainable, eliminating the need for hand-tuning separate modules and allowing joint learning of rate and distortion. The disparity-aided approach ensures the structural integrity of the reconstructed light fields. Comparisons with the state of the art show encouraging performance in terms of PSNR and MS-SSIM metrics. Also, there is a notable gain in the encoding and decoding runtimes. Source code is available at https://moha23.github.io/LF-DAAE.

翻译：商业全光照相机中的透镜阵列有助于在一次接触中捕捉光线的空间和角光线信息。虽然光场数据产生的高度维度使其具有超强能力,但也妨碍其广泛采用。因此,迫切需要高效压缩光场图像。现有解决方案通常由几个单独的模块组成,其中一些模块可能不是为光场数据的具体结构和质量设计的。这增加了编码器的复杂性,并导致不切实际的解码运行时间。我们提出了一个新的基于学习的、差异辅助模型,用于压缩4D光场图像,能够平行解码。该模型是端到端可训练的,不再需要手调不同模块,并允许联合学习速度和扭曲。差异辅助方法确保重建光场的结构完整性。与艺术状况的比较显示PSNR和MS-SSIM测量仪的绩效。此外,在可平行解码的4D光场图像压缩方面,我们提出了一种基于学习的、有差异的辅助模型。该模型可以端到端可训练,消除手调不同模块的需要,并允许联合学习速度和扭曲。差异辅助方法确保重建光场的结构性的完整。与艺术状况的比较显示PSNRRR和MS-SSIMIM的成绩。此外,还有,在加密和DADADADA/DA节中的可操作中有一个显著的源代码。