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.

Keywords

• light field image quality
• subjective experience
• semi-supervised learning
• metric mapping