## 6 Experiment Evaluation
### System Function Evaluation  
To verify the functional effectiveness of the IAPS accurately and objectively, we used the InceptionResNet-V2 aesthetic evaluation model [34] trained on the AVA dataset [28] to simulate the mean opinion score (MOS) of each image and output its image quality score. First, we extracted the feature maps of each block in the inception network, concatenated them, and used a regression model to predict the MOS value of the image. In Figure 13, the numbers in square brackets below each image represent the MOS predicted by the aesthetic evaluation model, reflecting the aesthetic quality of the image.  
To improve the reliability of the functional test results, we designed a questionnaire survey that compared the before and after aesthetic processing images as reference options and asked ordinary users to make aesthetic choices to verify the practicality of this system. In the questionnaires for users, each class contains three images: the original image, the Redmi K30-processed image, and the image processed by IAPS. According to the images' scenes, the IAPS processes images from four aspects: contrast, saturation, portrait beauty, and contrast saturation. To illustrate these four processing aspects more conveniently, we select a scene for each aspect and use it as an example to represent this processing aspect. We thus select four most common scenes in daily life, respectively taking city, food,  
Figure 11: Training and validation accuracy.  
Figure 12: Standard deviation of the accuracy of ShuffleNet.  
people, and mountain as examples to show the contrast, saturation, portrait beauty and contrast saturation processing of images in this study. The ordinary users we randomly interviewed in public places are different in age, personality, occupation, etc., so they are more universal. We first divide the images in Figure 13 into four groups, (1), (2), (3), and (4), according to the corresponding rows. Then, we organize a questionnaire survey with 100 participants. The 100 participants vote on the aesthetics of these four groups of images, and select the image they think is the best quality. The findings show that most of the participants believed that the aesthetic beauty of the processed images was superior, thereby attesting to the system's aesthetic processing method being more aligned with the general public's aesthetic standards. These results further substantiate the efficacy of this feature for ordinary users and its practical applicability. The statistical results of the questionnaire survey are presented in Figure 14.  
Figure 14: Statistics of the questionnaire survey results.  
Figure 13: Image aesthetic processing function test (**a**): Original image, (**b**): Redmi K30 processed image, (**c**): IAPS processed image).  
We also used the one-key beautification function on a state-of-the-art smartphone's Redmi K30 to process the images for experimental comparison, thereby highlighting the beautification effect of the model, as shown in Figure 13b. Figure 13 shows both the score comparison before and after IAPS processing and a subjective comparison with the beauty function of mainstream mobile phone systems. Improving image attributes for specific scene images through scene classification can significantly improve aesthetic and visual effects. The experimental results validated the effectiveness of the functionality of the system.