[1] Nizami I F, Rehman M U, Majid M et al. Natural scene statistics model independent no-reference image quality assessment using patch based discrete cosine transform[J]. Multimed Tools Appl, 79, 26285-26304(2020).

[2] Yang J C, Bian Z L, Zhao Y et al. Full-reference quality assessment for screen content images based on the concept of global-guidance and local-adjustment[J]. IEEE Trans Broadcast, 67, 696-709(2021).

[3] Wang B, Bai Y Q, Zhu Z J et al. No-reference light field image quality assessment based on joint spatial-angular information[J]. Opto-Electron Eng, 51, 69-81(2024).

[4] Bai Y Q, Zhu Z J, Zhu C H et al. Blind image quality assessment of screen content images via fisher vector coding[J]. IEEE Access, 10, 13174-13181(2022).

[5] Yan J B, Fang Y M, Liu X L. The review of distortion-related image quality assessment[J]. J Image Graphics, 27, 1430-1466(2022).

[6] Yang H, Fang Y M, Lin W S. Perceptual quality assessment of screen content images[J]. IEEE Trans Image Process, 24, 4408-4421(2015).

[7] Shao F, Gao Y, Li F C et al. Toward a blind quality predictor for screen content images[J]. IEEE Trans Syst Man Cybern Syst, 48, 1521-1530(2018).

[8] Yang J C, Zhao Y, Liu J C et al. No reference quality assessment for screen content images using stacked autoencoders in pictorial and textual regions[J]. IEEE Trans Cybern, 52, 2798-2810(2022).

[9] Ni Z K, Ma L, Zeng H Q et al. ESIM: edge similarity for screen content image quality assessment[J]. IEEE Trans Image Process, 26, 4818-4831(2017).

[10] Chen C L Z, Zhao H M, Yang H et al. Full-reference screen content image quality assessment by fusing multilevel structure similarity[J]. ACM Trans Multimedia Comput Commun Appl, 17, 1-21(2021).

[11] Jiang X H, Shen L Q, Ding Q et al. Screen content image quality assessment based on convolutional neural networks[J]. J Vis Commun Image Represent, 67, 102745(2020).

[12] Jiang X H, Shen L Q, Feng G R et al. An optimized CNN-based quality assessment model for screen content image[J]. Signal Process Image Commun, 94, 116181(2021).

[13] Zuo L X, Wang H L, Fu J. Screen content image quality assessment via convolutional neural network[C], 2082-2086(2016). https://doi.org/10.1109/ICIP.2016.7532725

[14] Jiang X H, Shen L Q, Yu L W et al. No-reference screen content image quality assessment based on multi-region features[J]. Neurocomputing, 386, 30-41(2020).

[15] Gao R, Huang Z Q, Liu S G. Multi-task deep learning for no-reference screen content image quality assessment[C], 213-226(2021). https://doi.org/10.1007/978-3-030-67832-6_18

[16] Zhang C F, Huang Z Q, Liu S G et al. Dual-channel multi-task CNN for no-reference screen content image quality assessment[J]. IEEE Trans Circuits Syst Video Technol, 32, 5011-5025(2022).

[17] Yang J C, Bian Z L, Zhao Y et al. Staged-learning: assessing the quality of screen content images from distortion information[J]. IEEE Signal Process Lett, 28, 1480-1484(2021).

[18] Pan L L, Shao J F. Multi-resolution point cloud completion fusing graph attention[J]. Laser Technol, 47, 700-707(2023).

[19] Chang Y L, Li S M, Liu A Q et al. Quality assessment of screen content images based on multi-stage dictionary learning[J]. J Vis Commun Image Represent, 79, 103248(2021).

[20] Huang Z Q, Liu S G. Perceptual hashing with visual content understanding for reduced-reference screen content image quality assessment[J]. IEEE Trans Circuits Syst Video Technol, 31, 2808-2823(2021).

[21] Yao Y, Hu J T, Yang W M et al. Distortion-aware mutual constraint for screen content image quality assessment[C], 403-414(2023). https://doi.org/10.1007/978-3-031-46305-1_33

[22] Rehman M U, Nizami I F, Majid M. DeepRPN-BIQA: deep architectures with region proposal network for natural-scene and screen-content blind image quality assessment[J]. Displays, 71, 102101(2022).

[23] Min X K, Gu K, Zhai G T et al. Screen content quality assessment: overview, benchmark, and beyond[J]. ACM Comput Surv, 54, 187(2022).

[24] Dosovitskiy A, Beyer L, Kolesnikov A et al. An image is worth 16×16 words: transformers for image recognition at scale[C](2021).

[25] Ren L H, Huang L W, Tian X et al. Multivariate long-term series forecasting method with DFT-based frequency-sensitive dual-branch transformer[J]. J Comput Appl, 44, 2739-2746(2024).

[26] Chen L, Zhang H W, Xiao J et al. SCA-CNN: spatial and channel-wise attention in convolutional networks for image captioning[C], 6298-6306(2017). https://doi.org/10.1109/CVPR.2017.667

[27] Yu L L, Zhang X B, Wang K. CMAAC: combining multiattention and asymmetric convolution global learning framework for hyperspectral image classification[J]. IEEE Trans Geosci Remote Sens, 62, 5508518(2024).

[28] Ding X H, Guo Y C, Ding G G et al. ACNet: strengthening the kernel skeletons for powerful CNN via asymmetric convolution blocks[C], 1911-1920(2019). https://doi.org/10.1109/ICCV.2019.00200

[29] Chen B L, Zhu H W, Zhu L Y et al. Deep feature statistics mapping for generalized screen content image quality assessment[J]. IEEE Trans Image Process, 33, 3227-3241(2024).

[30] Zhang W. No-reference quality assessment method for screen content image based on multi-scale convolutional neural network[J]. J Liaoning Univ Technol Nat Sci Ed, 44, 286-291(2024).

[31] Guo J L, Zhi M, Yin Y J et al. Review of research on CNN and visual Transformer hybrid models in image processing[J]. J Front Comput Sci Technol, 19, 30-44(2025).