# End-to-End Light Field Image Compression with Multi-Domain Feature Learning
## 3 Method
### EPI-Based Angle Completion Module  
The angle domain feature obtained by feature extractor only contains angle information within a limited disparity range. To improve the coding performance of large disparity light field images, an E-ACM is designed by fully exploiting the large disparity angle information contained in the EPI domain. This module takes the angle feature \(f_{A}\) and the  
Figure 1: Architecture of MFLFIC-Net. The original image \(x\) first passes through the encoder, which consists of a series of feature extractors, E-ACM, and SAJTM, to obtain a compact latent representation \(y\). Specifically, the E-ACM consists of three EPI feature refine blocks (EFRBs), and the SAJTM consists of two correlation learning-based joint transform blocks (CL-JTBs). After obtaining the reconstructed latent representation \(\widehat{y}\) from entropy coding, the reconstructed light field image \(\widehat{x}\) is finally generated through the decoder.  
EPI domain feature \(f_{E}\) as inputs, and obtains a complete angle feature \(f_{A^{*}}\), containing angle information on the complete disparity range.  
As shown in Figure 1, the E-ACM consists of three EPI feature refine blocks (EFRBs) and a concatenation operation. Specifically, the EFRB is designed to obtain the complementary feature \(f_{E^{*}}\) from \(f_{E}\). Then, \(f_{E^{*}}\) and the input \(f_{A}\) are concatenated in the channel dimension to obtain the complete angle feature \(f_{A^{*}}\). The specific calculation process is as follows:  
\[f_{A^{*}}=f_{E^{*}}\oplus f_{A}, \tag{1}\]  
\[f_{E^{*}}=E(f_{A},f_{E}), \tag{2}\]  
where \(E(\cdot)\) consists of three EPI feature refine blocks, and \(\oplus\) denotes the channel-wise concatenation.  
The architecture of the proposed EFRB is shown in Figure 2. Taking an EFRB as an example, the input angle domain feature \(f_{A}^{in}\) and the EPI domain feature \(f_{E}^{in}\) are first fed into two residual blocks to generate features \(f_{A}^{{}^{\prime}}\) and \(f_{E}^{{}^{\prime}}\). Then, a mutual attention mechanism is applied to model the similarity relationship between \(f_{A}^{{}^{\prime}}\) and \(f_{E}^{{}^{\prime}}\). Subsequently, nonlinear transform is performed after channel-wise concatenation to obtain the common feature \(f_{EA}\). The above process can be formulated as  
\[f_{E}^{{}^{\prime}}=R(f_{E}^{in}), \tag{3}\]  
\[f_{A}^{{}^{\prime}}=R(f_{A}^{in}), \tag{4}\]  
\[f_{EA}=h_{L}(F_{L}(f_{A}^{{}^{\prime}},f_{E}^{{}^{\prime}})\oplus f_{E}^{{}^{ \prime}}), \tag{5}\]  
where \(F_{L}(\cdot)\) denotes the mutual attention function [31]. \(h_{L}(\cdot)\) consists of two convolution layers and an activation layer, and \(\oplus\) denotes the channel-wise concatenation. \(R(\cdot)\) is composed of two residual blocks. Finally, the complementary feature \(f_{E^{*}}^{out}\) with large disparity angle information is extracted from \(f_{E}^{{}^{\prime}}\). The specific calculation process is as follows:  
\[f_{E^{*}}^{out}=g_{L}(f_{E}^{{}^{\prime}}-f_{EA}), \tag{6}\]  
where \(g_{L}(\cdot)\) consists of a convolution layer and an activation layer.  
As described above, \(f_{A^{*}}\) is obtained after \(f_{A}\) and \(f_{E}\) pass through E-ACM. Benefiting from the proposed E-ACM, the large disparity angle information contained in the EPI domain is fully explored, thereby improving the compression efficiency of large disparity light field images.  
Figure 2: Illustration of EPI feature refine block.