## 1 Introduction  
Given the subjective and multidimensional nature of evaluating the quality of color prints, this task becomes inherently complex. With the rapid evolution of the digital printing industry, ensuring high-quality prints has become a central concern, garnering increasing attention. Unlike expert measurements using technical means, the assessment of print image quality by the general public often relies on personal preferences and subjective perceptions [1]. Consequently, there is a pressing need to develop an evaluation metric that closely aligns with human visual perception. Numerous research teams have dedicated substantial efforts to evaluating both print quality and image quality. [PERSON] et al. [2] have developed an image processing and analysis metric employing a universal approach. This metric takes digital and scanned images as input and produces grayscale spatial visualizations indicating the location and severity of defects as output, effectively assessing various flaws. [PERSON] [3] applied No-Reference image quality assessment (NR-IQA) metrics to print images and evaluated the performance of several cutting-edge NR-IQA metrics on a vast array of printed photographs. Notably, the Blind Image Quality Index (BIQI) and the Natural Image Quality Evaluator (NIQE) centers outperformed other IQA methods. Of these, the NIQE displayed lower sensitivity to image content. In a related field of image processing pertinent to printing, [PERSON] [4] proposed a distortion-agnostic no-reference (NR) quality assessment metric leveraging natural scene statistics. This spatial domain-based metric offers heightened computational efficiency. [PERSON] [5] introduced a pioneering deep blind IQA metric based on multiple instance regression to overcome the fundamental limitation of unavailable ground truth for local blocks in traditional convolutional neural network-based image quality assessment (CNN-based IQA) models. Additionally, [PERSON] [6] devised a multiscale information content-weighted approach based on the natural image gradient structural similarity metric (GSM) model. This novel weighting method enhances the performance of IQA metrics relying on the Peak Signal-to-Noise Ratio (PSNR) and the SSIM. The researchers mentioned above have made significant advancements in their respective fields. However, methodologies for evaluating the quality of digital print images often assess images from a limited perspective. While addressing specific issues such as color deviation, spatial domain information may adequately characterize the problem. However, metrics representing image quality extend beyond color aspects alone. Different image contents emphasize various directions, and the importance of different aspects of image quality varies across different media. For example, while glossiness [3] is not an intrinsic attribute of digital images, it significantly influences the perceived quality of printed images. Additionally, the unique characteristics of digital print images have been overlooked in image quality assessment: the color gamut range of digital print images is much smaller than that of digital images, and printed images are essentially halftone images, with grayscale variations influenced by the human eye's low-pass characteristics, unlike continuous-tone digital images. Understanding the visual traits of the human eye reveals its ability to perceive diverse visual sensations through image contrast, brightness, and phenomena such as visual masking and brightness adaptation when viewing images. Therefore, evaluating print image quality requires considering not only objective assessments of print quality but also the influence of visual characteristics on print quality. Relying solely on a singular evaluation framework risks significant disparities between assessment conclusions on final print quality and intuitive visual perceptions, leading to notable discrepancies between objective and subjective quality assessments.