Focused plenoptic cameras can record spatial and angular information of the light field (LF) simultaneously with higher spatial resolution relative to traditional plenoptic cameras, which facilitate various applications in computer vision. However, the existing plenoptic image compression methods present ineffectiveness to the captured images due to the complex micro-textures generated by the microlens relay imaging and long-distance correlations among the microimages. In this paper, a lossy end-to-end learning architecture is proposed to compress the focused plenoptic images efficiently. First, a data preprocessing scheme is designed according to the imaging principle to remove the sub-aperture image ineffective pixels in the recorded light field and align the microimages to the rectangular grid. Then, the global attention module with large receptive field is proposed to capture the global correlation among the feature maps using pixel-wise vector attention computed in the resampling process. Also, a new image dataset consisting of 1910 focused plenoptic images with content and depth diversity is built to benefit training and testing. Extensive experimental evaluations demonstrate the effectiveness of the proposed approach. It outperforms intra coding of HEVC and VVC by an average of 62.57% and 51.67% bitrate reduction on the 20 preprocessed focused plenoptic images, respectively. Also, it achieves 18.73% bitrate saving and generates perceptually pleasant reconstructions compared to the state-of-the-art end-to-end image compression methods, which benefits the applications of focused plenoptic cameras greatly. The dataset and code are publicly available at https://github.com/VincentChandelier/GACN.

本文提出了一种损失端到端学习的架构，用于高效地压缩聚焦光学图像，包括数据预处理方案，全局注意力模块和新的图片数据集，可有效地捕捉聚焦透镜图像的复杂纹理和微图像之间的长距离关联，通过在重采样处理中计算像素级向量注意力来捕获特征图之间的全局相关性，实验证明，该方法在Focused plenoptic cameras中具有很高的效果，并且比HEVC和VVC的帧平均降低62.57％和51.67％的比特率，比最新的端到端图像压缩方法节省了18.73％的比特率，并且产生具有感知愉悦性的重建

利用微图像预处理和全局注意力学习聚焦光场图像压缩