# End-to-End Light Field Image Compression with Multi-Domain Feature Learning
## 2 Related Works
### Learning-Based Light Field Image Compression  
With the rapid development of deep learning, deep learning technology has been widely applied to various image processing tasks and has achieved significant success. Inspired by this, some researchers have devoted to studying learning-based light field image compression methods [26; 27; 28]. The learning-based compression methods can be divided into two categories: using deep learning tools to enhance the traditional light field image compression framework and using deep learning frameworks to perform end-to-end compression of light field images.  
In terms of deep learning tools, these methods have achieved impressive coding performance improvements by embedding deep learning tools into traditional light field image compression frameworks. [PERSON] et al. [26] proposed a light field image compression method based on view synthesis, using Versatile Video Coding (VVC) to compress the sparsely sampled SAIs, and introduced the efficient dual discriminator GAN model to generate the remaining SAIs at the decoding side. [PERSON] et al. [27] introduced a multi-view joint enhancement network to suppress compression artifacts, thereby improving coding performance. To further reduce bit consumption, [PERSON] et al. [28] proposed a downsampling-based light field image encoding framework that downsamples the light field image at the encoder and introduces a super-resolution network at the decoder. However, these methods only enhance a single module using deep learning tools, lacking the capability to achieve comprehensive optimization of the compression framework.  
In recent years, end-to-end image compression methods have been extensively studied and achieved impressive performance. As a key component, entropy coding plays a vital role in the encoding and decoding stages of data. Researchers continue to optimize the use of entropy coding to improve compression efficiency and image quality while reducing data transmission and storage costs. For example, [PERSON] et al. [29] proposed a hyperprior entropy model based on side information, which assists in the parameter generation of the entropy model by capturing the hidden information of the latent representation. On the basis, [PERSON] et al. [30] proposed an autoregressive context model that accurately capture the correlation between pixels in an image by fully utilizing contextual information, thereby achieving more effective image compression. These end-to-end methods usually adopt a compression autoencoder framework, which is overall optimized by the rate-distortion (RD) function. Inspired by this, some researchers have devoted themselves to developing end-to-end light field image compression methods. For instance, [PERSON] et al. [20] proposed a novel learning-based disparity assist model to compress four-dimensional light field images, which ensures the structural integrity of reconstructed light field images by introducing disparity information. [PERSON] et al. [18] introduced a data structure adaptive 3D-convolutional autoencoder, which effectively remove artifacts generated during the compression process and achieve better compression efficiency. [PERSON] et al. [19] proposed a spatial-angle decoupling network that reduces redundancy in spatial and angle features, respectively, thereby improving the compression efficiency. However, the above light field image compression methods cannot effectively exploit multi-domain features and their correlation, which prevents further improvement in compression efficiency.