## 4 Experimental Results and Analysis  
To assess the effectiveness of the proposed metrics, experiments were conducted on two standard databases: TID2013 [37] and TID2008 [38]. Both datasets comprise a  
Figure 11: Diagram of the MLP neural network architecture. selection of distorted images that are utilized to evaluate the perceptual quality of images. The TID2008 dataset comprises 25 reference images along with their respective distorted versions. The distortions in TID2008 encompass noise, blur, compression artifacts, and various other types of distortions. The TID2013 dataset comprises 3000 reference images paired with their corresponding distorted versions. The distortions applied to these images encompass a range of common types, including noise, blur, compression artifacts, and others. Four key performance metrics were employed for a quantitative evaluation: the Pearson Linear Correlation Coefficient (PLCC), SROCC, the Kendall Rank Order Correlation Coefficient (KROCC), and the Root Mean Square Error (RMSE). The PLCC gauges the model's prediction accuracy, reflecting its ability to predict subjective assessments with minimal error. The RMSE measures the consistency of the model's predictions. The SROCC and the KROCC indicate the monotonicity of the model's predictions, showcasing how well it can predict subjective assessments. The proposed method underwent comparison with nine classical Full-Reference image quality assessment (FR-IQA) methods, including GMSD [17], GSM [16], IFC [39], MAD [40], MSSIM [41], PSNR [42], SSIM [14], VIF [43], and VSI [44]. The color space transformation is performed on both the original digital image and the scanned print image, as illustrated in Figure 12:  
Using the TID2013 dataset as an example, the images in the TID2013 dataset were printed and subsequently scanned to obtain scanned images. The relationship between the chromaticity similarity index in the CIELAB space and the MOS was validated, utilizing the MOS provided by the dataset itself, according to the method described in Section 2.1. Figure 13 illustrates the distribution of the chromaticity similarity and the MOS for printed images in the CIELAB color space:  
Figure 12: The visualization of the image after being transformed into the CIELAB color space: (**a**) The scanned print image, (**b**) the scanned image’s L channel, (**c**) the scanned image’s A channel, (**d**) the scanned image’s B channel, (**e**) the original image, (**f**) the original image’s L channel, (**g**) the original image’s A channel, and (**h**) the original image’s B channel. Figure 13: A scatter plot of the image chromaticity similarity and the MOS in the CIELAB color space. The scatter plot depicts a consistently distributed set of data points, which are densely clustered, suggesting the precision of the method outlined in Section 2.1. Similarly, this study examines the correlation between the gradient similarity metric proposed in Section 2.2 and the MOS. Figure 14 illustrates the distribution of gradient similarity against the MOS:  
The scatter plot shows a relatively uniform distribution of data points with high concentrations, indicating the accuracy of the method proposed in Section 2.2. Randomly selected images from the TID2008 and TID2013 standard datasets were used to measure the SSIM values of the pre-print digital images and the scanned images, along with their energy histograms, as shown in Figure 15. It can be observed that the energy distribution of the two images is nearly identical in the low-frequency region. However, in the high-frequency region, the energy of the pre-print digital image is higher than that of the scanned image. This is because printing cannot perfectly reproduce continuous-tone images. Additionally, it can be concluded that the printing quality is poorer in the color depth region. This is reasonable because printing machines have a saturation point: once the ink density reaches a certain level, the color becomes saturated, and further increasing the ink amount will not change the color.