## 4 Datasets
### Image Aesthetic-Quality-Assessment Dataset  
Commonly used benchmark datasets for image aesthetic-quality assessment include AVA, AADB, Photo.net, etc. In recent years, researchers have proposed various datasets for image aesthetic-quality assessment, and Table 5 shows the specific introduction to and comparison of each dataset. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Dataset** & **Year** & \begin{tabular}{c} **Total Number of** \\ **Images** \\ \end{tabular} & \begin{tabular}{c} **Aesthetic** \\ **Grade** \\ \end{tabular} & \begin{tabular}{c} **Introduce** \\ \end{tabular} \\ \hline Photo.net [99] & 2011 & 20,278 & (0,7) & \begin{tabular}{c} Each image in the dataset has been rated by at least 10 people on a scale from 0 to 7, with 7 being the most aesthetically pleasing image. \\ \end{tabular} \\ \hline AVA [70] & 2012 & 255,530 & (1,10) & \begin{tabular}{c} Each image was rated by 78 to 549 raters with scores ranging from 1 to 10. The average score was used as the truth label for each image. The dataset authors labeled each image with one-to-two semantic tags, for a total of sixty-six semantic tags in text form for the entire dataset. \\ \hline AADB [100] & 2016 & 10,000 & (1,5) & \begin{tabular}{c} Images are scored by five raters on a scale of 1 to 5, with each image having an overall score and 11 aesthetic-attribute scores. \\ \end{tabular} \\ \hline PCCD [101] & 2017 & 4235 & (1,10) & \begin{tabular}{c} The dataset is more comprehensively labeled, containing evaluation scores, distributions and multi-person verbal comments for one overall and six aesthetic factors, each with a rating range of 1 to 10, ultimately being normalized to [0,1]. \\ \hline AROD [102] & 2018 & 380,000 & - & \begin{tabular}{c} The dataset is calculated by capturing the number of views and comments on each image from Flickr to obtain an aesthetic score. \\ \end{tabular} \\ \hline AVA-Reviews [103] & 2018 & 40,000 & - & \begin{tabular}{c} Each image follows six linguistic comments, which are labeled without regard to multiple aesthetic factors. \\ \end{tabular} \\ \hline DPC-Captions [104] & 2019 & 154,384 & - & \begin{tabular}{c} The dataset contains annotations of up to five aesthetic attributes of an image through knowledge transfer from the fully annotated small-scale dataset PCCD. \\ \end{tabular} \\ \hline EVA [105] & 2020 & 4070 & - & \begin{tabular}{c} A minimum of 30 votes per image was included to assess the difficulty of the aesthetic rating, the rating of the 4 complementary aesthetic attributes, and the relative importance of each attribute in forming the aesthetic opinion. \\ \end{tabular} \\ \hline IDEA [106] & 2020 & 9191 & (0,9) & \begin{tabular}{c} The dataset is distributed almost in equilibrium, with 1000 images for each score from 0 to 8 and 191 images for a score of 9. \\ \end{tabular} \\ \hline TAD66K [107] & 2022 & 66,327 & (1,10) & \
\begin{tabular}{c} The dataset contains 47 popular themes, and each image has been intensively annotated by more than 1200 people using specialized thematic evaluation criteria. \\ \end{tabular} \\ \hline AMD-A [108] & 2023 & 16,924 & (0,1) &
\begin{tabular}{c} The dataset was divided into two groups, one (11,166 images) for the overall aesthetic score regression and the other (16,924 images) for the classification and regression of the aesthetic attribute scores. \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparison of image aesthetic-quality-evaluation datasets. Photo.net, AROD datasets are collected through online image-sharing scoring sites and have high-quality data labelling. The AVA, AADB, PCCD, IDEA, and TAD66K datasets were collected using manual scoring. Among these, the AVA dataset is labelled as high quality and supports the learning of aesthetic classification, aesthetic scoring, and aesthetic distribution, but it does not take into account the effects brought by the image-shooting scene, camera parameters, and post processing.