## 5 Conclusions  
This paper presents an effective and reliable metric for assessing the quality of printed images that closely mimic human visual perception. Compared to mainstream single-dimensional evaluation metrics, the metrics proposed in this paper offer a comprehensive evaluation of printed images from multiple dimensions, significantly improving the accuracy and reliability of the assessment. It combines spatial and frequency domain features, starting with data analysis in the CIELAB color space. In the spatial domain, it extracts color and gradient features, calculates the similarity of different features using similarity measures, and then computes structural similarity. Subsequently, it applies Gabor transform and discrete cosine transform (DCT) to the images, utilizing texture and spatial frequency features as complementary aspects of printed image quality. Multiscale and multi-directional texture features, along with spatial frequency features, are employed for a comprehensive analysis of the frequency domain information of the images, and similarity measures are used to assess similarity at the frequency domain level. Finally, an MLP neural network regression tool is utilized to train a stable model through extensive training, predicting the overall quality scores of the printed images. Extensive experiments on publicly available databases demonstrate that the selected parameters and this method highly conform to subjective perception, exhibiting high consistency with human visual characteristics. Samples in publicly available databases may lack diversity in certain aspects, as some databases might focus primarily on specific types of prints or particular printing conditions. This limitation could result in inadequate generalization of the results to different types of prints or conditions. Future research should employ more diverse and comprehensive databases to ensure the broader applicability of the findings. Future research directions include enhancing the accuracy and robustness of evaluation models to more comprehensively reflect print quality. One promising avenue is to explore the application of deep learning and reinforcement learning in print quality assessment. For instance, convolutional neural networks (CNN) can be used to extract image features, while reinforcement learning can be employed to optimize the evaluation process. Improving the self-learning and adaptive capabilities of these models will further enhance the efficiency and effectiveness of the assessments. Methodology, J.Z. ; software, J.Z. ; validation, J.Z., H.Z., F.X. and Y.Z. ; data curation, J.Z. ; writing--original draft preparation, J.Z. ; writing--review and editing, J.Z., H.Z., F.X. and Y.Z. ; visualization, J.Z. All authors have read and agreed to the published version of the manuscript. This research was funded by national key plan, grant number 2018 YFB1309401. Not applicable. The data presented in this study are available on request from the corresponding author due to protect the integrity and accuracy of the research. * (20) [PERSON]; [PERSON] Ensemble of deep convolutional neural networks with Gabor face representations for face recognition. _IEEE Trans. Image Process._**2019**, _29_, 3270-3281. [CrossRef]
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