Abstract
Blind/no-reference image quality assessment (IQA) aims to provide a quality score for a single image without references. In this context, deep learning models can capture various image artifacts, which made significant progress in this study. However, current IQA methods generally utilize the pre-trained convolution neural networks (CNNs) on classification tasks to obtain image representations, which do not perfectly represent the quality of images. In order to solve this problem, this paper uses semi-supervised representation learning to train a quality-sensitive encoder (QS-encoder), which can extract image features specifically for image quality. Intuitively, this feature is more conducive to train the IQA model than the feature used for classification tasks. Thus, QS-encoder is plunged into a carefully designed hyper network to build a quality-sensitive hyper network (QS-hyper) to solve IQA tasks in more general and complex environments. Extensive experiments on the public IQA datasets show that our method outperformed most state-of-art methods on both Pearson linear correlation coefficient (PLCC) and Spearman’s rank correlation coefficient (SRCC), and it made 3% PLCC improvement and 3.9% SRCC improvement on TID2013 datasets. Therefore, it proves that our method is superior in capturing various image distortions, which meets a broader range of evaluation requirements.
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References
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 (2017)
Bromley, J., Guyon, I., LeCun, Y., Säckinger, E., Shah, R.: Signature verification using a “siamese" time delay neural network. Adv. Neural Inf. Process. Syst. 6, 737–744 (1993)
Caron, M., Bojanowski, P., Joulin, A., Douze, M.: Deep clustering for unsupervised learning of visual features. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 132–149 (2018)
Chen, X., He, K.: Exploring simple siamese representation learning. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 15750–15758 (2021)
Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., Fei-Fei, L.: Imagenet: a large-scale hierarchical image database. In: 2009 IEEE conference on computer vision and pattern recognition, pp. 248–255. IEEE (2009)
Ghadiyaram, D., Bovik, A.C.: Massive online crowdsourced study of subjective and objective picture quality. IEEE Trans. Image Process. 25(1), 372–387 (2015)
He, K., Fan, H., Wu, Y., Xie, S., Girshick, R.: Momentum contrast for unsupervised visual representation learning. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9729–9738 (2020)
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 770–778 (2016)
Hjelm, R.D., et al.: Learning deep representations by mutual information estimation and maximization. arXiv preprint arXiv:1808.06670 (2018)
Hosu, V., Lin, H., Sziranyi, T., Saupe, D.: Koniq-10k: an ecologically valid database for deep learning of blind image quality assessment. IEEE Trans. Image Process. 29, 4041–4056 (2020)
Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Adv. Neural Inf. Process. Syst. 25, 1097–1105 (2012)
Lin, K.Y., Wang, G.: Hallucinated-iqa: no-reference image quality assessment via adversarial learning. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 732–741 (2018)
Liu, X., Van De Weijer, J., Bagdanov, A.D.: Rankiqa: learning from rankings for no-reference image quality assessment. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1040–1049 (2017)
Mittal, A., Moorthy, A.K., Bovik, A.C.: No-reference image quality assessment in the spatial domain. IEEE Transactions on Image Processing 21(12), 4695–4708 (2012)
Moorthy, A.K., Bovik, A.C.: A two-step framework for constructing blind image quality indices. IEEE Signal Process. Lett. 17(5), 513–516 (2010)
Ou, F.Z., et al.: Sdd-fiqa: unsupervised face image quality assessment with similarity distribution distance. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 7670–7679 (2021)
Ponomarenko, N., et al.: Image database tid2013: peculiarities, results and perspectives. Signal Process. Image Commun. 30, 57–77 (2015)
Su, S., et al.: Blindly assess image quality in the wild guided by a self-adaptive hyper network. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 3667–3676 (2020)
Xue, W., Mou, X., Zhang, L., Bovik, A.C., Feng, X.: Blind image quality assessment using joint statistics of gradient magnitude and laplacian features. IEEE Trans. Image Process. 23(11), 4850–4862 (2014)
Zhang, L., Zhang, L., Bovik, A.C.: A feature-enriched completely blind image quality evaluator. IEEE Trans. Image Process. 24(8), 2579–2591 (2015)
Zhang, X., Yu, W., Jiang, N., Zhang, Y., Zhang, Z.: Sps: A subjective perception score for text-to-image synthesis. In: 2021 IEEE International Symposium on Circuits and Systems (ISCAS), pp. 1–5. IEEE (2021)
Zhang, X., Zhang, Y., Zhang, Z., Yu, W., Jiang, N., He, G.: Deep feature compatibility for generated images quality assessment. In: Yang, H., Pasupa, K., Leung, A.CS., Kwok, J.T., Chan, J.H., King, I. (eds.) Neural Information Processing. ICONIP 2020. Communications in Computer and Information Science, vol. 1332. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-63820-7_40
Zhang, Y., Zhang, X., Zhang, Z., Yu, W., Jiang, N., He, G.: No-reference quality assessment based on spatial statistic for generated images. In: Yang, H., Pasupa, K., Leung, A.CS., Kwok, J.T., Chan, J.H., King, I. (eds.) Neural Information Processing. ICONIP 2020. Communications in Computer and Information Science, vol. 1332. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-63820-7_57
Zhu, H., Li, L., Wu, J., Dong, W., Shi, G.: Metaiqa: deep meta-learning for no-reference image quality assessment. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 14143–14152 (2020)
Acknowledgement
This research is supported by Sichuan Science and Technology Program (No. 2020YFS0307, No. 2020YFG0430, No. 2019YFS0146), Mianyang Science and Technology Program (2020YFZJ016).
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Zhang, X., Zhang, Y., Yu, W., Nie, L., Jiang, N., Gong, J. (2021). QS-Hyper: A Quality-Sensitive Hyper Network for the No-Reference Image Quality Assessment. In: Mantoro, T., Lee, M., Ayu, M.A., Wong, K.W., Hidayanto, A.N. (eds) Neural Information Processing. ICONIP 2021. Lecture Notes in Computer Science(), vol 13111. Springer, Cham. https://doi.org/10.1007/978-3-030-92273-3_26
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