# Attention-Based Color Difference Perception for Photographic Images
## 4 Experiment
### Ablation Study  
To analyze the effectiveness of CD-Attention, ablation experiments are conducted on the key modules using the SPCD. This includes modules for feature extraction and attention guidance.  
Feature Extraction Module. The feature extraction module is a crucial part of the network, and the quality of feature extraction directly affects the network's predictions and outputs. Common feature extraction backbones were compared with the results shown in Table 3. The compared backbones include Resnet50 [42], Resnet101 [42], Resnet152 [42], HRnet [45], InceptionResNetV2 [46], and ViT-B/16 and ViT-B/8 [31]. The feature extraction model combining Resnet50 [42] and ViT-B/16 [31] is found to be the most effective. There is a noted decrease in performance with the increase in depth of the CNN network. This is attributed to the fact that the deeper the CNN network, the more high-level features are captured. However, CD-Attention requires only the extraction of local features by the CNN  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **STRESS**\(\downarrow\) & **PLCC**\(\uparrow\) & **SRCC**\(\uparrow\) \\ \hline CIEDE2000 [6] & 18.203 & 0.73 & 0.751 \\ PieAPP [21] & 20.918 & 0.62 & 0.653 \\ LPIPS [22] & 15.42 & 0.816 & 0.804 \\ DISTS [23] & 15.235 & 0.821 & 0.805 \\ CD-Net [28] & 15.962 & 0.801 & 0.826 \\ CD-Flow [29] & 14.110 & 0.837 & 0.832 \\ CD-Attention & 15.626 & 0.851 & 0.846 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of the generalization performances on TID2013.  
network, with the ViT responsible for extracting global and deep features. When comparing the Transformer backbone networks ViT-B/8 [31] and ViT-B/16 [31], the performance of ViT-B/16 [31] is superior.  
Attention Guidance Module. An ablation study was conducted on the attention guidance module of CD-Attention to verify the effectiveness of introducing deformable convolution for attention guidance. As shown in Table 4, the first method employs deformable convolution for attention guidance as used in CD-Attention, which combines CNN features with ViT global semantic features and then proceeds with feature fusion. The second method uses traditional feature concatenation to directly merge CNN features with ViT features. In CD measurement tasks, the first method significantly outperforms the second, demonstrating that the proposed approach of attention guidance before feature fusion effectively integrates local texture features with global semantic features. This further proves that the global semantic features extracted by the ViT can guide the local shallow features extracted by CNN, bringing the CD measurement results closer to human perception. This further proves that the approach of using global semantic features to guide the local shallow features extracted by CNN is correct. The third and fourth methods in the table utilize only Resnet50 and ViT-B/16 as the backbone for which the performance of their models drops significantly. This proves that the features from Resnet50 and ViT-B/16 significantly affect the performance of CD-Attention, demonstrating that both local texture features and global semantic features are essential in CD measurement tasks. It also further confirms that the design concept of the proposed CD-Attention model is correct.