# End-to-End Light Field Image Compression with Multi-Domain Feature Learning
## 1 Introduction  
With the development of light field imaging technology, light field images have been widely used in many fields, including depth estimation [1], view synthesis [2], and free viewpoint rendering [3]. However, the dense viewpoints and high-resolution characteristics of light field images pose huge challenges to transmission. Consequently, many compression methods have been researched to improve the compression efficiency of light field images. Traditionally, many light field image compression methods based on standard video codecs have been studied, which perform specific processing on light field images and compress them using existing video codecs [4; 5]. Specifically, the sub-aperture images (SAIs) of the light field are first organized into pseudo video sequences with a specific order. Subsequently, existing video codecs are applied to compress these pseudo video sequences. Finally, the reconstructed pseudo video sequences are rearranged to recover the original light field SAIs. Although these methods can effectively reduce the redundancy of light field images, the effect becomes limited when the number of SAIs increases. Considering the similarity among views in light field images, some researchers have studied compression frameworks based on view synthesis, which encode sparse views and synthesizes the remaining views based on the disparity relationship at the decoder [6; 7; 8]. Since existing standard video codecs are not specifically tailored for light field images, it is difficult for the above methods to achieve optimal coding efficiency. Recently, deep learning technology has developed rapidly and achieved impressive success in various tasks, such as quality enhancement [9; 10], content generation [11; 12], and object detection [13; 14]. Inspired by this, researchers have explored many deep learning-based video coding methods [15; 16; 17]. For light field image coding, some end-to-end codingframeworks have also been proposed, and preliminary results have been achieved [18; 19; 20; 21]. Specifically, the first step involves the extraction of light field image features. Subsequently, a nonlinear transform is applied to light field image features to obtain a compact latent representation for efficient entropy coding. Therefore, the coding performance of end-to-end light field image compression methods mainly depends on the complete extraction of light field image features and the efficient compression of image features. In order to completely extract light field image features, several methods have been proposed, such as data structure adaptive 3D convolution (DSA-3D) [18], space-angle decoupling mechanism [19], and disparity-aware model [20]. However, the aforementioned methods mainly extract spatial feature and angle feature within a limited disparity range and ignore the exploitation of the large disparity angle information in the EPI domain. As a result, this problem generally hinders the efficient compression of light field images with large disparity. Meanwhile, some technologies have been designed for the efficient compression of light field image features by reducing unnecessary redundancy. For example, [PERSON] et al. [21] introduced generative adversarial networks (GANs) into light field image compression, which utilize decoded sparse sampling SAIs to generate remaining light field SAIs at specific locations, thus effectively reducing the transmission of redundant information. [PERSON] et al. [19] decoupled the spatial feature and angle feature in the light field image and reduced the redundancy of the spatial and angle features, respectively. However, these methods usually focus on reducing the redundancy within a single domain and ignoring correlations among multi-domain features, which leads to the insufficient reduction of redundancy. To address the above problems, a novel multi-domain feature learning-based light field image compression network (MFLFIC-Net) is proposed in this paper.