# Evaluation Model of Holographic Communication Experience in the 6G Era Based on Light Field Display
###### Abstract  
Holographic communication is considered one of the typical scenarios in the 6G era. Studies have shown that the light field display is considered the most effective naked-eye 3D display method in the 6G era. Despite this, there are still many issues worthy of study. Since there are currently no experience-evaluation standards for holographic communications proposed worldwide, this also causes a lot of research work to remain at the design level and vision. To truly realize the holographic communication scenario, it is necessary to systematically evaluate the light field display technology. The level of user experience determines the value of the holographic communication scenario. This requires quantifying the user's experience level and mapping it to the technical parameters of the light field display image. However, there is still room for improvement in related research. This paper proposes a model based on semi-supervised learning, which takes light field image data of various scenes as input and uses three experience scores of comfort, space, and realism as output to complete the subjective experience evaluation of light field images. Compared with evaluation methods that focus on the quality of the image itself, this article focuses more on the effect on human experience. Compared with existing work, this paper makes improvements in two respects: feature engineering and training strategies. In terms of feature selection, the convolutional neural network is used to extract image content features, and the image quality parameter-extraction module is used to extract image property features. The two are spliced as the input of the classifier; in terms of the training strategy, pseudo-labels and dynamic thresholds are used for training. The final experimental results show that on the MPI-LFA data set, the comfort dimension's classification accuracy is 80.21, the spatial dimension's classification accuracy is 83.12, and the realism dimension's classification accuracy is 81.88.  
light field image quality; subjective experience; semi-supervised learning; metric mapping 2023131328132  
As the topic of the metaverse becomes more and more popular, people are also finding that the light field display is a technical method that is very consistent with the concept of the metaverse. Some people even call the light field display the \"entrance to the metaverse\". The light field can be regarded as \"discrete\" and \"digital\" holography. As the angular resolution and viewpoint resolution of the light field continue to improve, the display effect of the light field will continue to approach holographic display [2]. With the development of various 3D display technologies [3; 4; 5; 6; 7; 8; 9; 10; 11; 12; 13], the light field has gradually become an ideal 3D display technology. Currently, 6G research has evolved from vision to implementation [14]. The evaluation of the implementation effect still depends on people's experience. [PERSON] [15] proposed experience indicators for holographic communication and stipulated three dimensions of experience. The mapping from the original light field image to the experience level will be beneficial to the evaluation of experience, the value judgment of the scene, and the calculation of communication performance requirements. However, the research in this part is not yet complete. Therefore, this paper proposes a model based on semi-supervised learning, which takes light field image data of multiple scenes as input and uses three experience scores as output to complete the subjective experience evaluation of light field images. The innovation of this article is that a deep learning model is designed to map the level of user experience with light field images. Compared with the traditional approach, in feature engineering, light field image content features, two-dimensional plane features, and light field features are introduced for combination.