Full reference image quality assessment based on dual-space multi-feature fusion
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Publisher
Springer Science and Business Media LLC
Link
https://link.springer.com/content/pdf/10.1007/s00530-024-01353-5.pdf
Reference35 articles.
1. Yang, F., Yang, C., An, P., et al.: 360 video quality assessment based on saliency-guided viewport extraction. Multimedia Syst. 30, 89 (2024). https://doi.org/10.1007/s00530-024-01285-0
2. No-Reference Screen Content Image Quality Assessment With Unsupervised Domain Adaptation: B. Chen, H. Li, H. Fan and S. Wang. IEEE Trans. Image Process. 30, 5463–5476 (2021). https://doi.org/10.1109/TIP.2021.3084750
3. S. Aghapour Maleki and H. Ghassemian.: Spatial Quality Assessment of Pansharpened Images Based on Gray Level Co-Occurrence Matrix. 2022 International Conference on Machine Vision and Image Processing (MVIP), Ahvaz, Iran, Islamic Republic of, 1–6 (2022). https://doi.org/10.1109/MVIP53647.2022.9738763
4. Kim, D., Lee, S., Kim, C.: Contextual Information Based Quality Assessment for Contrast-Changed Images. IEEE Signal Process. Lett. 26(1), 109–113 (2019). https://doi.org/10.1109/LSP.2018.2882916
5. Seo, S., Ki, S., Kim, M.: A Novel Just-Noticeable-Difference-Based Saliency-Channel Attention Residual Network for Full-Reference Image Quality Predictions. IEEE Trans. Circuits Syst. Video Technol. 31(7), 2602–2616 (2021). https://doi.org/10.1109/TCSVT.2020.3030895
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