# Attention-Based Color Difference Perception for Photographic Images
## 4 Experiment
### Main Results  
Dataset validation results. As shown in Table 1, CD-Attention is compared with 30 common CD measurement algorithms. These methods can be categorized into five groups based on their application scenarios and algorithmic characteristics. The first category mainly includes methods aimed at CD measurement for natural images: CIELAB [4], CIE94 [36], CMC [5], CIEDE2000 [6], CIECAM02 [37], CIECAM16 [38], S-CIELAB [7], Imai01 [8], Toet03 [9], Lee05 [10], Hong06 [11], Ouni08 [12], Simone09 [13], and Pedersen12 [39]. The second category consists of typical algorithms for image quality assessment: SSIM [18], VSI [19], LIP [20], PieAPP [21], LPIPS [22], and DISTS [23]. The third category includes just noticeable difference (JND) measures: Chou07 [40], Lissner12 [16], and Butteraugli [17]. Additionally, classic CNN networks such as VGG [41], ResNet-18 [42], UNet [43], and CAN [44] are trained, and their performance in CD measurement is tested. CD-Attention is compared with the state-of-the-art methods CD-Net [28] and CD-Flow [29]. From the table, the following conclusions can be drawn: First, traditional CD calculation methods designed for natural images do not perform particularly well on images captured by smartphones. The reason is that the direct computation of image CDs does not align with the human perception of CDs, as subjective ratings tend to favor regions of interest within the image. Among these, CIELAB [4], CIE94 [36], CIEDE2000 [6], CIECAM02 [37], CIECAM16 [38], S-CIELAB [7], and Ouni08 [12] perform relatively better because they all calculate CDs based on the CIELAB color space. Second, typical image quality assessment algorithms such as SSIM [18], VSI [19], LIP [20], PieAPP [21], LPIPS [22], and DISTS [23] have been proven effective in the field of image quality assessment, capable of identifying and detecting common image distortions. However, they do not perform well on CD image datasets because changes in image quality due to CDs are not necessarily perceived as image distortions, which also confirms the hypothesis proposed in the paper. Third, CD measurement algorithms based on JND can perceive the smallest changes detectable by  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multirow{2}{*}{ \begin{tabular}{c} **Color** \\ **Space** \\ \end{tabular}  } & \multicolumn{3}{c}{**Perfectly Aligned Pairs**} & \multicolumn{3}{c}{**Non-Perfectly Aligned Pairs**} & \multicolumn{3}{c}{**All**} \\ \cline{3-11}  & & **STRESS \(\downarrow\)** & **PLCC \(\uparrow\)** & **SRCC \(\uparrow\)** & **STRESS \(\downarrow\)** & **PLCC \(\uparrow\)** & **SRCC \(\uparrow\)** & **STRESS \(\downarrow\)** & **PLCC \(\uparrow\)** & **SRCC \(\uparrow\)** \\ \hline CIELAB [4]  
humans, but they perform poorly on the SPCD dataset, with low correlation and STRESS metrics due to a poor perception of changes above the threshold in the dataset. Fourth, training models directly with classic CNN networks yields better results than traditional algorithms and image quality assessment algorithms as neural networks have strong generalization capabilities, and transfer learning through training on the SPCD dataset achieves more accurate CD perception results. This proves that using CNN networks for feature extraction in CD perception is feasible. Fifth, most algorithms perform well on aligned datasets but poorly on misaligned datasets due to insufficient adaptability to geometric changes in images. Sixth, compared with the state-of-the-art methods CD-Net and CD-Flow, the CD-Attention's correlation metrics PLCC and SRCC are significantly higher than the current best algorithms. This is because CD-Net and CD-Flow are designed based on CNN networks, mainly extracting local texture features without combining global features. In summary, CD-Attention has achieved state-of-the-art performance, accurately perceiving the quality loss in images due to CDs and showing a high correlation with human ratings. Generalization Analysis.