Multi-scale Transformer with Decoder for Image Quality Assessment
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Publisher
Springer Nature Singapore
Link
https://link.springer.com/content/pdf/10.1007/978-981-99-8850-1_18
Reference27 articles.
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2. Kang, L., Ye, P., Li, Y., Doermann, D.: Convolutional neural networks for no-reference image quality assessment. In: 2014 IEEE Conference on Computer Vision and Pattern Recognition, Columbus, OH, USA, 2014, pp. 1733–1740 (2014). https://doi.org/10.1109/CVPR.2014.224
3. Zhang, W., Ma, K., Yan, J., Deng, D., Wang, Z.: Blind image quality assessment using a deep bilinear convolutional neural network. IEEE Trans. Circuits Syst. Video Technol. 30(1), 36–47 (2020). https://doi.org/10.1109/TCSVT.2018.2886771
4. Bosse, S., Maniry, D., Müller, K.-R., Wiegand, T., Samek, W.: Deep neural networks for no-reference and full-reference image quality assessment. IEEE Trans. Image Process. 27(1), 206–219 (2018). https://doi.org/10.1109/TIP.2017.2760518
5. Liu, X., Van De Weijer, J., Bagdanov, A.D.: RankIQA: learning from rankings for no-reference image quality assessment. In: 2017 IEEE International Conference on Computer Vision (ICCV), Venice, Italy, 2017, pp. 1040–1049 (2017). https://doi.org/10.1109/ICCV.2017.118
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