## 2 Extraction of Spatial Domain Features from Digital Print Images  
For assessing the quality of printed images, there exist several methods that can capture post-print images, including the use of multispectral cameras, CCD line scan cameras, and scanners. In this study, we opted for the Epson Expression 13000 XL scanner (Epson, Suwa, Japan) to acquire image data due to its high precision, which facilitates the faithful reproduction of real data, along with its user-friendly and efficient operation. The specific parameters are detailed in Table 1. The acquired images are in the RGB color space. We derived the modulation transfer function (MTF) of the scanner by scanning the ISO 12233 [7] test chart, as illustrated in Figure 1 The scanning results allowed us to determine the scanner's MTF, as shown in Figure 2. At lower spatial frequencies, the MTF values are higher, indicating that the scanner performs well when reproducing larger details and overall image contrast. The curve drops gently, so we can conclude that the device's performance is relatively stable. Figure 3 presents a novel metric proposed for assessing the quality of digital print images, termed Spatial-Frequency Domain Feature Fusion (FPSF). The preprocessing stage involves the feature curve transformation of pre-print digital images, wherein the images undergo processing using device ICC curves. Additionally, post-scan images undergo registration and correction. Subsequently, the preprocessed images are subjected to spatial transformation, transitioning from the original color space to the CIELAB color space. Features are then extracted from the L, A, and B channels, with chromaticity features being extracted from channels A and B. In the L channel, gradient features are computed using Sobel filters, structural features are evaluated using the structural similarity index (SSIM) metric, texture features are extracted via a Gabor filter bank, and spatial frequency features are derived by analyzing discrete cosine transform (DCT) coefficients of image blocks. Following feature extraction, similarity metrics are applied to quantify the differences between features extracted from pre-print digital images and scanned print images. Finally, an MLP neural network model trained on standard datasets is employed to predict the objective quality scores of the print images. Due to inevitable issues like positional shifts and rotations during the scanning process, image registration is necessary to ensure more accurate scanned images. In this study, we utilized the SURF (Speeded-Up Robust Features) metric. Extensive research by [PERSON] [8] has shown this metric to be highly reliable. Since the imaging principles of pre-press digital images and post-scan images differ, it is illogical to directly compare them. Spatial transformations and device characteristic curve transformations were applied to pre-press images, including both forward and inverse transformations, as depicted in Figure 4. \begin{table}
\begin{tabular}{c c} \hline \hline
**Technical Parameters** & **Value** \\ \hline Transparency Area & 309 mm \(\times\) 420 mm \\ Optical Density & 3.8D \\ Color Bit Depth & 48 bit \\ Photosensitive Element & 12-line color CCD with micro lenses \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The technical specifications of the Epson Expression 13000 XL Scanner. Figure 2: The modulation transfer function (MTF) of the Epson Expression 13000 XL Scanner. Here, A.ICC represents the spatial transformation curve, while B.ICC denotes the device characteristic curve. The forward and reverse transformations do not preserve the original image; instead, they introduce color alterations due to the application of different ICC curves, thereby simulating the printing imaging process accurately. For color image prints, accurately reproducing colors serves as a critical benchmark for quality control. In color image printing, challenges often arise in mapping color information between different color spaces.