# Evaluation Model of Holographic Communication Experience in the 6G Era Based on Light Field Display
## 6 Conclusions  
Currently, 6G research is highly discussed around the world. As one of the typical scenarios of 6G, holographic communication's final implementation effect still depends on the scene effect, that is, the human/user experience level. However, there is currently a lack of research in related aspects, and the quantification of experience levels is not perfect, which makes the research on scenarios stay at the design level and vision level. This article attempts to answer this question. This paper proposes a mapping model based on this supervised learning to connect the light field image with the user's experience score. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Feature Name** & **Realism** & **Feeling of Space** & **Comfort** \\ \hline CNN content features & \(-34.10\%\) & \(-41.30\%\) & \(-27.50\%\) \\ Structural similarity & \(-5.02\%\) & \(-0.01\%\) & \(-5.02\%\) \\ Peak signal-to-noise ratio & \(-6.24\%\) & \(-0.36\%\) & \(-3.17\%\) \\ Spatial domain features & \(-7.08\%\) & \(-9.30\%\) & \(-6.30\%\) \\ Angle domain features & \(-5.20\%\) & \(-11.00\%\) & \(-2.50\%\) \\ Null-angle coupling relationship characteristics & \(-8.10\%\) & \(-12.02\%\) & \(-6.02\%\) \\ Projection domain features & \(-4.45\%\) & \(-6.51\%\) & \(-5.42\%\) \\ \hline \hline \end{tabular}
\end{table} 
Table 4: The impact of each feature on the results. The final results show that in the three descriptive dimensions of realism, comfort, and space, the final classification accuracy of the model reaches more than 80%, which also verifies that the quality of the light field image is indeed related to the user experience. Regarding the influence results of various features, we found that the influence of the image content features produced by the CNN module has a more obvious impact on the final result. This is because the subjects are inevitably affected by the image content when scoring. This also reminds us that if we want to obtain a more accurate evaluation of experience effects, we need to classify and discuss the scenarios. For example, some complex scenes will give people a stronger sense of space. We also found that as an evaluation of light field images, the influence of the four light field characteristics is also relatively obvious. This also confirms that the quality assessment of light field images and the quality assessment of traditional 2D images have different effects on the experience. Whether viewed from the spatial domain or the angle domain, the introduction of light field image features has a significant improvement effect on the final evaluation of the experience effect. Nonetheless, there are some areas for improvement in this work. For example, in previous research, the number of viewpoints in a light field is an important indicator that affects experience. However, the light field display device Lookingglass is a device with a fixed display resolution and a number of viewpoints. There is no way to further increase the number of viewpoints. Of course, we can conduct simulation experiments by deliberately reducing the number of viewpoints, but too low a number of viewpoints has no value in practical applications, so it is not taken into account. For another example, the training method of dynamically revising the threshold proposed in this article allows the threshold to decrease as the training progresses. However, there must be a more appropriate and efficient threshold-control method, which is also one of the directions to be explored in the next step of this topic. Also, when extracting content features with CNN, due to the large scale of the light field image, this also brings a huge amount of calculation to the convolution process. However, many studies have shown that replacing the entire light field with central sub-aperture images or other representative position images also provides good results. This is also one of the directions for future improvement, which can reduce a large number of convolution operations. In the testing in this article, the number of subjects was limited.