## 4 Experiment and Result Analysis
### Qualitative Analysis of Experimental Results  
In this section, we conducted a qualitative analysis of the experimental results to demonstrate the effectiveness of our global aesthetic image generation algorithm. We compared the images generated from different latent spaces with the original images, as well as compared the original images with aesthetically edited images of different semantics. These comparisons were conducted to showcase the effectiveness of our algorithm in generating aesthetic images. To better capture the aesthetic information in the generated aesthetic images, we generated 10,000 images and used them to verify the effectiveness of the proposed aesthetic image generation algorithm. Based on the 22 global aesthetic decision faces obtained in Section 3.2.3, the \(Z\) and \(W\) latent spaces of the 10,000 generated images were aesthetically edited, respectively, and the corresponding global aesthetic images were obtained. After observation, the generated images obtained by editing on different latent spaces had significant visual differences. Therefore, the qualitative and quantitative analysis of the global aesthetic generation effect on the \(Z\) and \(W\) latent space was conducted first. Figure 10 shows an example of the change in the image during the linear aesthetic editing of \(Z\) and \(W\) of StyleGAN's generated church. Where \(G\) represents the generative model, \(w\) represents a latent space in \(W\) space, \(z\) represents a latent space in \(Z\) space, \(y=G(W)\) represents the image obtained from the original \(w\) input \(G\), \(y_{3}=G(w+3n)\) pushes the \(w\) vector in three steps in the direction of aesthetic evaluation promotion, and so on. The image aesthetic effects generated in the \(W\) and \(Z\) latent spaces are rather different and, in the same latent space, the number of aesthetic steps is different, and the effect also presents a big gap. The larger the number of steps, the stronger the aesthetic degree. Figure 10: The example of the church with \(Z\) and \(W\) editing processes. Figure 9: The change histogram of the rate in beautifying images. Figure 11 shows the results of \(w\) (line 1), 3, 4, 6, 8, 10, and \(z\) (line 2) in the direction of aesthetic evaluation, respectively. It can be seen that \(W\)-based aesthetic editing has a much less semantic impact on the original generated images than \(Z\)-based aesthetic editing. The editing of the \(W\) hidden space causes little semantic deformation of the image, and the overall structure is almost consistent with the original image. The final image generated by \(Z\) is rather different from the original image, but compared with the original image, both achieved the aesthetic quality improvement effect visible to the naked eye. At the same time, red impurities can be observed in \(Z\) in the gradual editing process. After observing many examples of \(Z\), the probability of impurities in editing is much higher than for \(W\). The following figure shows some examples of \(W\) and \(Z\) generation. In this figure, based on the generated image of the \(W\) latent space, the overall semantic structure of the cat is almost unchanged, but visually, the image quality is better. Although the image of the cat based on \(Z\) space is still a cat, the overall structure of the image has changed greatly, and the body of the cat has disappeared. Thus, the aesthetic image based on the \(W\) latent space is more in line with our requirements. And we analyze the global aesthetic image generation results based on editing results on W latent space. Figure 12 shows a partial sample of the aesthetic image generated based on the algorithm proposed in this study. Most of the aesthetic improvement makes the details of the image richer, the color becomes more in line with the public aesthetics, or it eliminates part of the noise. There are different degrees of aesthetic improvement visible to the naked eye.