## 3 Methods for Assessing the Aesthetic Quality of Images
### Traditional Methods of Assessing the Aesthetic Quality of Images  
Most of the research on non-deep-learning-based image aesthetic-quality assessment has focused on extracting aesthetic features of images, mainly by manually constructing features and then using classifiers or extractors. [PERSON] [51] was the first to use image features to achieve a quantitative evaluation of image aesthetic quality. Although non-deep learning methods for evaluating the aesthetic quality of images have achieved some results and proved the feasibility and effectiveness of computable aesthetics, the relevant features still have major shortcomings in representing the aesthetics of images. The following surveys several non-deep-learning-based aesthetic-evaluation methods in recent years. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multirow{2}{*}{**Pub. Year**} & \multirow{2}{*}{**Backbone**} & \multicolumn{2}{c}{**Experiments**} & \multirow{2}{*}{**Major Contributions**} \\ \cline{3-3} \cline{5-6}  & & & **Datasets** & & & \\ \hline DeepLabv1 [43] & 2014 & VGG-16 & Pascal VOC 2012 & 71.6 & \begin{tabular}{c} Atrous convolution, fully connected \\ CRFs \\ \end{tabular} \\ \hline FCN [39] & 2015 & VGG-16 & Pascal VOC 2011 & 62.7 & \begin{tabular}{c} Pioneer of end-to-end semantic \\ segmentation \\ \hline PSPNet [41] & 2017 & \begin{tabular}{c} VGG- \\ 16/ResNet101 \\ \end{tabular} & \begin{tabular}{c} Pascal VOC 2012 \\ \end{tabular} & 85.4 & \multirow{4}{*}{Spatial Pyramid Pooling Module} \\ \cline{3-3} \cline{5-6}  & & & Cityscapes & 80.2 & \\ \hline DeepLabv2 [44] & 2017 & ResNet50 & \begin{tabular}{c} Pascal VOC 2012 \\ \end{tabular} & 79.7 & \multirow{4}{*}{Proposed Atrous Spatial Pyramid Pooling (ASPP)} \\ \cline{3-3} \cline{5-6}  & & & Cityscapes & 70.4 & \\ \hline DeepLabv3 [45] & 2017 & ResNet101 & \begin{tabular}{c} Pascal VOC 2012 \\ \end{tabular} & 86.9 & \multirow{4}{*}{Cascade or parallel ASPP modules} \\ \cline{3-3} \cline{5-6}  & & & Cityscapes & 81.3 & \\ \hline DeepLabv3+ [46] & 2018 & Xception & \begin{tabular}{c} Pascal VOC 2012 \\ \end{tabular} & 89.0 & \multirow{4}{*}{Added an upsampled decoder} \\ \cline{3-3} \cline{5-6}  & & & Cityscapes & 82.1 & \\ \hline DANet [47] & 2019 & ResNet101 & Pascal VOC 2012 & 82.6 & \begin{tabular}{c} Dual attention: positional attention \\ module and channel attention \\ module \\ \end{tabular} \\ \hline STDC [48] & 2021 & STDC2 & \begin{tabular}{c} ImageNet \\ \end{tabular} & 76.4 & \multirow{4}{*}{Proposed detail-aggregation module} \\ \cline{3-3} \cline{5-6}  & & & Cityscapes & 77.0 & \\ \cline{3-3} \cline{5-6}  & & & CamVid & 73.9 & \\ \hline FPANet [49] & 2022 & ResNet18 & Cityscapes & 75.9 & \begin{tabular}{c} Using ResNet and Atrous Spatial \\ Pyramid Pooling (ASPP) to extract \\ more advanced semantic information \\ \end{tabular} \\ \hline DECANet [50] & 2023 & ResNet101 & \
\begin{tabular}{c} Pascal VOC 2012 \\ \end{tabular} & 81.0 &
\begin{tabular}{c} Introducing Effective Channel \\ Attention Networks (ECANet) at the \\ Encoder \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison and analysis of neural network-based layout analysis methods. In the study of image aesthetic-quality assessment, researchers were the first to investigate both image aesthetic features such as composition basis, rule of thirds, and depth of field [52; 53] and perceptual features such as color, edge, texture, and semantics [54; 55] to achieve aesthetic-quality assessment [2; 56]. [PERSON] [53] used visual perception to establish a visual salient region model and proposed a method based on the visual aesthetic-quality-assessment method of images based on salient region enhancement. Some researchers applied techniques related to image processing to extract a series of underlying image features to describe the aesthetic properties of images from the underlying features of images and build aesthetic-assessment models to achieve image aesthetic-quality assessment [57; 51; 58; 59; 60].