# Evaluation Model of Holographic Communication Experience in the 6G Era Based on Light Field Display
## 2 Related Work  
Image quality assessment (IQA) [16; 17] is a fundamental problem in image processing and computer-vision-related fields. Its development process has evolved from an early image quality database containing only a small number of image data and a single distortion type to the current image quality database with a rich variety of distortion types and a large number of image data, from the early combination of feature engineering and traditional machine learning algorithms to the current end-to-end deep learning model and from the relatively single evaluation indicators in the early days to the diversified application-related evaluation indicators today. The most mainstream method is currently the end-to-end deep learning method [18]. The image-feature-extraction model and mapping model are obtained through end-to-end learning. The core of this type of model lies in the convolutional neural network (CNN). [PERSON] et al. [19] introduced CNN into the design of the NR-IQA model earlier and proposed the CNNIQA (convolutional neural network for image quality assessment) model. This model only contains one layer of CL, one pooling layer, and two layers of FCLs. [PERSON] et al. [20] proposed an NR-IQA model called DIQA (deep image quality assessment), which is trained in two stages. Among them, the first stage trains a CNN that can learn objective error maps; the second stage uses human subjective scores to fine-tune the CNN model. [PERSON] et al. [21] proposed an NR-IQA model based on a dual-branch CNN structure. The two branches process the input image and the corresponding gradient image, respectively. Finally, the feature vectors processed by the two branches are fused, and after FCLs, the input is the image quality fraction. [PERSON] et al. [22] proposed a bilinear NR-IQA model (deep bilinear convolutional neural network, DBCNN), which is suitable for the synthetic distortion and real distortion image-quality prediction. The model feature-extraction module contains two branches, which are shallow CLs and a VGG16 (VisualGeometry Group) model [23]. It uses bilinear fusion to fuse the output feature maps of the two branches and then performs quality prediction through FCLs. The above models all directly learn the mapping relationship between images and subjective scores, which is also the same type of problem to be solved in this article. The main research problem this article hopes to solve is the mapping relationship between the light field image data transmitted by holographic communication and the user experience level, which is essentially a light field image data-classification problem based on deep learning. The acquisition cost of subjective data labels is very high, so a semi-supervised learning strategy is planned to be used to complete the research. The main methods of semi-supervised learning (SSL) can be divided into two categories: the pseudo-labeling method and the consistency regularization method. Pseudo-labeling refers to the method of using the model itself to obtain labels for unlabeled data. Specifically, the softmax probability distribution output by the model is regarded as a soft pseudo-label, and the prediction obtained by the argmax or one-hot is regarded as a hard pseudo-label. These pseudo-labels are utilized as supervised losses to further train the model. Consistency regularization is an important part of many SSL algorithms. The idea of consistency regularization is that the classifier should output the same class distribution probability for unlabeled samples even after noise is injected into them. That is, it is enforced that an unlabeled sample should be classified into the same category as its own enhanced sample [24]. The problem faced in this question is that different categories of image data only have differences in image quality, and the differences in image content are very small.