# Evaluation Model of Holographic Communication Experience in the 6G Era Based on Light Field Display
## 4 Results  
The resource used in this experiment was a PC. It is equipped with Intel(R) Core(TM) i7, NVIDIA 3080 Ti GPU. The development environment was Python 3.10, and the deep learning development framework used included Pytorch 2.0.1 and other auxiliary tool packages such as sklearn, numpy, and pandas.  
In our model structure, the CNN uses two layers of convolution and two layers of maximum pooling. The numbers of convolution kernels in the convolution layer are 512 and 64. The following fully connected layer contains three hidden layers. The numbers of hidden layer units are 1024, 512, and 64. The output activation function is softmax, the learning rate is 0.001, and the optimizer uses stochastic gradient descent.  
In this experiment, 200 out of 1000 labeled data were used as a test set to verify the effect of the model. Using classification accuracy as the evaluation criterion for model effectiveness, the performance of the final model in the three classification dimensions is shown in Table 3. The results show that after parameter adjustment, the model performs well in all three evaluation dimensions.  
In addition, we conducted experiments to determine the impact of various features on the final classification accuracy. The final impact results are shown in Table 4. The negative percentage in the table represents how much the accuracy of the final result is reduced after removing the features of this dimension.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Dimension of Experience** & **Accuracy Rate** \\ \hline Third dimension & 81.88 \\ Spaciousness & 83.12 \\ Comfort & 80.21 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Model classification results.  
Figure 6: Optimization of training strategy.  
Structural similarity is a full-reference image-quality-evaluation index. It measures the similarity of two images in three respects,brightness, contrast, and structure, and models distortion as a combination of three different factors: brightness, contrast, and structure. The peak signal-to-noise ratio is also a commonly used objective standard to evaluate image quality. It is based on the mean square error between the original image and the processed image. In terms of spatial domain characteristics, the spatial definition distortion is characterized by the NSS distribution characteristics of the central sub-aperture map. In the angle domain, changes in angle consistency are represented by GLCM features on macropixels. In the space-angle coupling domain, the changes in the space-angle coupling relationship are characterized by the GLCM feature on the EPI. In the projection domain, the distortion of each focusing layer is characterized by the local entropy statistical characteristics on the refocusing map.  
As can be seen from the table of data above, two-dimensional features (peak signal-to-noise ratio and structural similarity) have little impact on the final experience. The image content features extracted by the CNN module have a greater impact. The influence of the four light field characteristics is also obvious. Compared with the other two evaluation dimensions, realism is more affected by the comprehensive impact of various characteristics. The spatial dimension is significantly affected by the content of the image itself and the light field characteristics. Comfort is more sensitive to the content and two-dimensional characteristics of the image itself.