# Attention-Based Color Difference Perception for Photographic Images
## 2 Related Works  
### CD Formula  
The purpose of CD measurement is to find a method that accurately quantifies and assesses the human eye's perception of CDs, so as to achieve consistency and accuracy in color measurement in practical applications. * Traditional Algorithms  
The study of traditional CD formulas began in the early 20 th century and can be roughly divided into three stages: early CD algorithms, improved CD algorithms, and image quality assessment methods [27]. The early CD measurement was mainly represented by CIELAB and CIELUV, which directly calculated CD in a uniform color space, and the calculation formula is as follows:  
\[\Delta E_{\text{ab}}^{*}=\sqrt{\left(L_{1}-L_{2}\right)^{2}+\left(a_{1}-a_{2} \right)^{2}+\left(b_{1}-b_{2}\right)^{2}} \tag{1}\]  
\(L_{1}\), \(a_{1}\), \(b_{1}\), and \(L_{2}\), \(a_{2}\), \(b_{2}\) are the brightness and color coordinates of two color samples in the standard color space, respectively. The limitation of this method is that CIELAB and CIELUV cannot adapt to the diversification of modern photographic image content, including different textures, patterns, and lighting conditions. They cannot accurately reflect the CDs in HDR images and cannot fully simulate the human eye's perception of CDs in complex images. With the advancement of color science, new CD measurement methods and models such as CMC, CIEDE2000, and S-CIELAB have been developed. These improvements include the weighting of brightness, chroma, and hue differences, the introduction of rotation factors to enhance predictions in specific hue areas, and improvements to computational efficiency. These enhancements allow the algorithms to provide more accurate CD measurements across various applications such as quality control, color design, and image processing. The CIEDE2000 calculation formula is as follows:  
\[\Delta E_{00}=\sqrt{\left(\Delta L^{\prime}\right)^{2}+\left(\Delta C^{\prime} \right)^{2}+\left(\Delta H^{\prime}\right)^{2}+\rho\cdot\left(\Delta L^{\prime }\right)(\Delta C^{\prime})+\sigma\cdot\left(\Delta C^{\prime}\right)(\Delta H ^{\prime})+\delta\cdot\left(\Delta L^{\prime}\right)(\Delta H^{\prime})} \tag{2}\]  
where \(\Delta L^{\prime}\) indicates the luminance difference, \(\Delta C^{\prime}\) denotes the chroma difference, \(\Delta H^{\prime}\) signifies the hue difference, and the interaction terms \(\rho,\sigma\), and \(\delta\) represent the mutual influence of CDs. These interaction terms and weighting functions are used together to adjust CD measurements to better match the perception of the human eye. Although such methods have made significant advancements in simulating the human eye's perception of CDs, the choice of weighting functions may depend on specific application scenarios, requiring users to adjust according to the specific situation, increasing the difficulty of use. * Learning-Based CD Formulas  
In recent research, some scholars have proposed CD formulas based on deep learning, with representative methods being CD-Net [28] and CD-Flow [29]. The author of CD-Net first created a CD dataset for mobile phone images, SPCD, and proposed an end-to-end learnable CD formula based on lightweight neural networks. They extract features by performing convolutions and coordinate transformations on the original and distorted images, calculate the distance of the features to obtain the loss, and use this loss to train the network. Experiments have verified that CD-Net is superior to 33 existing CD formulas. CD-Flow, based on the foundation of CD-Net, has been improved by training a multi-scale autoregressive normalization flow for feature transform, which includes six scales of features and performs splitting and cascading operations, respectively. It uses the Euclidean distance, which is proportional to human perception of CD for CD calculation. Experiments have proven that its algorithm is currently the best, superior to CD-Net. The above two deep learning-based CD measurement algorithms perform significantly better than traditional algorithms and show good generalization.