# End-to-End Light Field Image Compression with Multi-Domain Feature Learning
## 4 Experiments
### Comparison Results  
A quantitative evaluation is performed to demonstrate the effectiveness of the proposed MFLFIC-Net, which compares MFLFIC-Net with an end-to-end light field image compression method SADN [19] and some traditional light field image compression methods, including GPR [24], HEVC reference software HM 16.9 [34], and VVC reference software VTM 10.0 [35]. It is worth mentioning that these comparison methods were selected because they have been widely used in previous related research and have achieved remarkable results. In addition, this selection covers the main methods and strategies in this field to ensure the reliability of the experimental results. Since our experimental settings are consistent with SADN [19], the experimental results of GPR [24] are obtained from the results of SADN [19]. For a fair comparison, the end-to-end light field compression method SADN [19] is retrained on the same experiment equipment as the proposed method. To objectively evaluate the compression performance, the BD-PSNR and BD-rate are utilized as the objective metrics, where HM 16.9 [34] is set as the baseline to calculate the BD-PSNR and BD-rate. As shown in Table 1, MFLFIC-Net achieves a 2.149 dB BD-PSNR increase and a 65.585% BD-rate reduction. Compared with the SADN [19], MFLFIC-Net further obtains a 0.354 dB BD-PSNR increase and a 21.329% BD-rate reduction. In addition, compared with VTM 10.0 [35], MFLFIC-Net further obtains a 1.128 dB BD-PSNR increase and a 30.028% BD-rate reduction. As shown in Table 1, the experimental results demonstrate that MFLFIC-Net achieves the highest rate-distortion performance, which verifies its effectiveness. Furthermore, the rate-distortion curves of the MS-SSIM and PSNR metrics are shown in Figure 4. In terms of PSNR metrics, MFLFIC-Net outperforms the other methods, demonstrating the effectiveness of MFLFIC-Net. Regarding the MS-SSIM metric, as shown in Figure 4, the MFLFIC-Net also exhibits optimal performance when compared to the other methods. In addition, since the images in the test dataset do not overlap with the training set and the images are different, the test experimental results are able to verify the generalization ability of the method. Experimental results show that the proposed method has better performance than the comparative methods, which further proves that our method has stronger generalization ability. To visually verify the efficiency of the proposed MFLFIC-Net, the reconstruction results generated by SADN [19] and the proposed MFLFIC-Net are shown in Figure 5. It is worth noting that, in the visual comparison, MFLFIC-Net is only compared with SADN [19]. This is because SADN [19] significantly outperforms the other compared methods. From the visualization results, it can clearly observe that the reconstruction texture of the proposed method is clearer than SADN [19]. This is because the proposed E-ACM provide angle information with a large disparity range, which is beneficial in reconstructing light field images. At the same time, the proposed MFLFIC-Net has a lower bit consumption, which indicates that SAJTM effectively reduce redundancy and improve performance. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Methods** & **BD-PSNR (dB)** & **BD-rate (\%)** \\ \hline GPR & \(-2.253\) & \(138.095\) \\ VTM 10.0 & \(1.021\) & \(-35.557\) \\ SADN & \(1.795\) & \(-44.256\) \\ Proposed & \(2.149\) & \(-65.585\) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison results of BD-rate and BD-PSNR on the EPFL dataset when using HM 16.9 as baseline. Figure 4: Rate-distortion curves. (**a**) Comparison of PSNR on the EPFL dataset. (**b**) Comparison of MS-SSIM on the EPFL dataset. The SAJTM is proposed to effectively reduce redundancy between spatial and complete angle features. As shown in Table 2 and Figure 6, with the replacement of the SAJTM, the proposed network achieves a 0.231 dB BD-PSNR reduction and a 10.615% BD-rate increase.