[1] Li, X., & Qiu, J. (2021). A multi-parameter video quality assessment model based on 3D convolutional neural network on the cloud. ASP transactions on internet of things, 1(2), 14–22.
[2] Bianco, S., Celona, L., Napoletano, P., & Schettini, R. (2018). On the use of deep learning for blind image quality assessment. Signal, image and video processing, 12, 355–362.
[3] Varga, D. (2019). No-reference video quality assessment based on the temporal pooling of deep features. Neural processing letters, 50(3), 2595–2608.
[4] Chen, P., Li, L., Wu, J., Dong, W., & Shi, G. (2021). Contrastive self-supervised pre-training for video quality assessment. IEEE transactions on image processing, 31, 458–471.
[5] Varga, D. (2022). No-reference video quality assessment using multi-pooled, saliency weighted deep features and decision fusion. Sensors, 22(6), 2209.
[6] Hong, C., Chen, X., Wang, X., & Tang, C. (2016). Hypergraph regularized autoencoder for image-based 3D human pose recovery. Signal processing, 124, 132–140.
[7] Xue, J., Yin, L., Lan, Z., Long, M., Li, G., Wang, Z., & Xie, X. (2021). 3D DCT based image compression method for the medical endoscopic application. Sensors, 21(5), 1817.
[8] Hosu, V., Hahn, F., Jenadeleh, M., Lin, H., Men, H., Szirányi, T., … & Saupe, D. (2017). The konstanz natural video database (konvid-1k). 2017 ninth international conference on quality of multimedia experience (qomex) (pp. 1–6). IEEE. https://doi.org/10.1109/QoMEX.2017.7965673
[9] Huynh, B. Q., Li, H., & Giger, M. L. (2016). Digital mammographic tumor classification using transfer learning from deep convolutional neural networks. Journal of medical imaging, 3(3), 34501.
[10] Vranješ, M., Rimac-Drlje, S., & Vranješ, D. (2018). Foveation-based content adaptive root mean squared error for video quality assessment. Multimedia tools and applications, 77, 21053–21082.
[11] Koike, M., Urata, Y., & Yamagishi, K. (2022). Bitstream-quality-estimation model for tile-based VR video streaming services. IEICE transactions on communications, 105(8), 1002–1013.
[12] Li, J., Zou, L., Yan, J., Deng, D., Qu, T., & Xie, G. (2016). No-reference image quality assessment using Prewitt magnitude based on convolutional neural networks. Signal, image and video processing, 10, 609–616.
[13] Li, X., Guo, Q., & Lu, X. (2016). Spatiotemporal statistics for video quality assessment. IEEE transactions on image processing, 25(7), 3329–3342.
[14] Li, Y., Po, L. M., Cheung, C. H., Xu, X., Feng, L., Yuan, F., & Cheung, K-W. (2015). No-reference video quality assessment with 3D shearlet transform and convolutional neural networks. IEEE transactions on circuits and systems for video technology, 26(6), 1044–1057.
[15] Villaret, M., & others. (2021). Efficient fundus image gradeability approach based on deep reconstruction-classification network. Artificial intelligence research and development: proceedings of the 23rd international conference of the catalan association for artificial intelligence (Vol. 339, p. 402). IOS Press. https://books.google.com/books?id=LYxJEAAAQBAJ&lr=&source=gbs_navlinks_s
[16] Koike, M., Urata, Y., Egi, N., & Yamagishi, K. (2021). Extension of itu-t p. 1204.3 model to tile-based vr streaming services. 2021 ieee international workshop technical committee on communications quality and reliability (cqr 2021) (pp. 1–6). IEEE. https://doi.org/10.1109/CQR39960.2021.9446237
[17] Saad, M. A., Bovik, A. C., & Charrier, C. (2011). DCT statistics model-based blind image quality assessment. 2011 18th ieee international conference on image processing (pp. 3093–3096). IEEE. https://doi.org/10.1109/ICIP.2011.6116319
[18] Saad, M. A., Bovik, A. C., & Charrier, C. (2014). Blind prediction of natural video quality. IEEE transactions on image processing, 23(3), 1352–1365.
[19] Saupe, D., Hahn, F., Hosu, V., Zingman, I., Rana, M., & Li, S. (2016). Crowd workers proven useful: a comparative study of subjective video quality assessment. QoMEX 2016: 8th international conference on quality of multimedia experience. konstanzer online-publikations-system (KOPS). http://nbn-resolving.de/urn:nbn:de:bsz:352-0-371921
[20] Schmidhuber, J. (2015). Deep learning in neural networks: An overview. Neural networks, 61, 85–117.