## 4 Experiment and Result Analysis
### Quantitative Analysis of Experimental Results  
In this section, we first conducted a quantitative analysis by comparing the image scores generated by editing the aesthetics on different latent spaces (W and Z) with the scores of the original images. And the experiments involved quantifying the average aesthetic scores and aesthetic image rates for both the original generated images and the aesthetically edited images using the NIMA (Neural Image Assessment) model. The corresponding improvement rates were also calculated. Furthermore, we used line graphs to visually demonstrate the relationship between image scores, image semantics, and the degree of aesthetic editing in the W latent space. Additionally, we provided evidence of the effectiveness of aesthetic generation through a histogram depicting the changes in the rate of aesthetically pleasing images. The experiment used the NIMA aesthetic evaluation model as the aesthetic quality evaluation index of the generated images, predicting the 10,000 test images. The aesthetic score of the generated images after \(Z\) editing, the aesthetic score of the generated images after \(W\) editing, and the aesthetic average score of 10000 images was calculated for comparison. Table 2 shows the three scores for each generated model. Looking at the data in the table, we can see that the aesthetic scores of the edited images were improved, whether through the aesthetic editing based on the \(Z\) or \(W\) implicit space. In contrast, the image aesthetic score improved more after latent spatial aesthetics editing based on \(W\). Figure 7 shows the aesthetic mean score contrast mixed histogram, where the yellow discount represents the initial NIMA score, the orange bar represents the aesthetic average score of the image generated based on \(Z\)-editing, and the blue bar represents the aesthetic average score of the image generated based on \(W\)-editing. It can be seen that \(Z\) greatly improves the aesthetic quality of the images generated by \(W\) editing compared with the original images. Moreover, from the histogram, it can be observed that the editing effect on \(W\) is almost entirely better than that on \(Z\), and only the aesthetic image generation of \"car\" is slightly less satisfactory than the editing effect on \(Z\) hidden space, which is consistent with subjective perception. The NIMA average aesthetic score for the original generated and aesthetic generated images are calculated in the experiment. The beauty rate was quantified, and the corresponding improvement rate was calculated. Detailed data are presented in Table 3; the \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & **Church** & **Bridge** & **Tower** & **Bedroom** & **Apartment** & **Cat** & **Car** & **Horse** \\ \hline Initial & 4.8189 & 4.916 & 4.9875 & 4.3932 & 4.5333 & 4.4196 & 5.1415 & 4.7391 \\ \hline \(Z\) & 5.4915 & 5.4606 & 5.3251 & 5.4279 & 5.5754 & 5.6024 & 5.9814 & 5.3482 \\ \hline W & 5.6506 & 6.0398 & 6.2284 & 5.6086 & 5.6986 & 5.7370 & 5.6924 & 5.3797 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The average score of the aesthetic grading. Figure 7: The mixed histograms of the NIMA scores contrast. algorithm achieved excellent results in improving the aesthetic average score. Comparing the aesthetic average score with the original, the first generated image was increased by 22.26%. The beauty rate also achieved an almost perfect effect, increasing the average beauty rate by 71%, and bridges, towers, and cars finally even reached 100%. Eight generating models were edited 10 times, and the aesthetic average score of each yuan editing image was recorded. Figure 8 shows from the original image of 10 yuan editing images in the process of aesthetic score changes. It can be clearly seen from the eight generating models that, with each yuan edit, the latent space is moved in the direction of aesthetic quality, as the aesthetic average score rises.Horses, churches, cats, and cars maintain the same increasing rate almost always, while the generative models gradually decrease.