A multimodal dense convolution network for blind image quality assessment
Author:
Publisher
Zhejiang University Press
Subject
Electrical and Electronic Engineering,Computer Networks and Communications,Hardware and Architecture,Signal Processing
Link
https://link.springer.com/content/pdf/10.1631/FITEE.2200534.pdf
Reference50 articles.
1. Bianco S, Celona L, Napoletano P, et al., 2018. On the use of deep learning for blind image quality assessment. Signal Image Video Process, 12(2):355–362. https://doi.org/10.1007/s11760-017-1166-8
2. Bosse S, Maniry D, Wiegand T, et al., 2016. A deep neural network for image quality assessment. Proc IEEE Int Conf on Image Processing, p.3773–3777. https://doi.org/10.1109/ICIP.2016.7533065
3. Bosse S, Maniry D, Müller KR, et al., 2018. Deep neural networks for no-reference and full-reference image quality assessment. IEEE Trans Image Process, 27(1):206–219. https://doi.org/10.1109/TIP.2017.2760518
4. Chockalingam N, Murugan B, 2023. Hierarchical patch selection: an improved patch sampling for no reference image quality assessment. IEEE Trans Artif Intell, early access. https://doi.org/10.1109/TAI.2023.3262623
5. Cheng ZX, Takeuchi M, Katto J, 2017. A pre-saliency map based blind image quality assessment via convolutional neural networks. Proc IEEE Int Symp on Multimedia, p.77–82. https://doi.org/10.1109/ISM.2017.21
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1. MFHOD: Multi-modal image fusion method based on the higher-order degradation model;Expert Systems with Applications;2024-09
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