Abstract
Nowadays, image quality assessment (IQA) is important for many applications, such as transmission, compression or reproduction. And no-reference image quality assessment (NRIQA) has received extensive attention because there is no need to use original images. Especially in this artificial intelligence and big data age, it is impossible to get the reference images every time. However, many conventional NR-IQA algorithms extracted features by hand and designed only for one specific distortion type. In this paper, we will propose a NR-IQA method to predict image quality accurately with great generalization ability. We first use gaussian kernel regression to do the data augmentation owing to the limited dataset. Then, a NR-IQA model based on convolutional neural network (CNN) is trained, named Deep No-Reference Image Quality Assessment (DNRIQA), including five convolutional layers and three pooling layers for feature extraction, and three fully connected layers for regression. Finally, the experiments show that this approach achieves the state-of-the-art performance on LIVE dataset, and further cross dataset experiments prove that the model has excellent generalization ability. Experimental results of this paper is very competitive compared with other algorithms.
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References
Sheikh, H.R., Sabir, M.F., Bovik, A.C.: A statistical evaluation of recent full reference image quality assessment algorithms. IEEE Trans. Image Process. 15(11), 3440–3451 (2006). A Publication of the IEEE Signal Processing Society
Larson, E.C., Chandler, D.M.: Most apparent distortion: full-reference image quality assessment and the role of strategy. J. Electron. Imaging 19(1), 011006 (2010)
Ponomarenko, N., Jin, L., Ieremeiev, O.: Image database TID2013: peculiarities, results and perspectives. Signal Process. Image Commun. 30, 57–77 (2015)
Wang, Z., Bovik, A.C., Sheikh, H.R.: Image quality assessment: from error visibility to structural similarity. IEEE Trans. Image Process. 13(4), 600–612 (2004). A Publication of the IEEE Signal Processing Society
Xue, W., Zhang, L., Mou, X.: Gradient magnitude similarity deviation: a highly efficient perceptual image quality index. IEEE Trans. Image Process. 23(2), 684–695 (2014)
Zhang, L., Zhang, L., Mou, X.: FSIM: a feature similarity index for image quality assessment. IEEE Trans. Image Process. 20(8), 2378 (2011)
Sheikh, H.R., Bovik, A.C., Veciana, G.D.: An information fidelity criterion for image quality assessment using natural scene statistics. IEEE Trans. Image Process. 14(12), 2117–2128 (2005)
Moorthy, A.K., Bovik, A.C.: Blind image quality assessment: from natural scene statistics to perceptual quality. IEEE Trans. Image Process. 20(12), 3350–64 (2011). A Publication of the IEEE Signal Processing Society
Mittal, A., Moorthy, A.K., Bovik, A.C.: No-reference image quality assessment in the spatial domain. IEEE Trans. Image Process. 21(12), 4695 (2012). A Publication of the IEEE Signal Processing Society
Doermann, D., Kang, L., Kumar, J., et al.: Unsupervised feature learning framework for no-reference image quality assessment. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 1098–1105. IEEE Computer Society (2012)
Kang, L., Ye, P., Li, Y., et al.: Convolutional neural networks for no-reference image quality assessment. In: Computer Vision and Pattern Recognition, pp. 1733–1740. IEEE (2014)
Bosse, S., Maniry, D., Wiegand, T., et al.: A deep neural network for image quality assessment. In: IEEE International Conference on Image Processing, pp. 3773–3777. IEEE (2016)
Narwaria, M., Lin, W.: SVD-based quality metric for image and video using machine learning. IEEE Trans. Syst. Man Cybern. Part B Cybern. 42(2), 347 (2012). A Publication of the IEEE Signal Processing Society
Saad, M.A., Bovik, A.C., Charrier, C.: Blind image quality assessment: a natural scene statistics approach in the DCT domain. IEEE Trans. Image Process. 21(8), 3339–52 (2012)
Girshick, R., Donahue, J., Darrell, T., et al.: Rich feature hierarchies for accurate object detection and semantic segmentation. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 580–587. IEEE Computer Society (2014)
Krizhevsky, A., Sutskever, I., Hinton, G E.: ImageNet classification with deep convolutional neural networks. In: International Conference on Neural Information Processing Systems, pp. 1097–1105. Curran Associates Inc. (2012)
Liang, Y., Wang, J., Wan, X., Gong, Y., Zheng, N.: Image quality assessment using similar scene as reference. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9909, pp. 3–18. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46454-1_1
Kim, J., Lee, S.: Fully deep blind image quality predictor. IEEE J. Sel. Top. Signal Process. 11(1), 206–220 (2017)
Li, Y., Po, L.M., Feng L, et al.: No-reference image quality assessment with deep convolutional neural networks. In: IEEE International Conference on Digital Signal Processing, pp. 685–689. IEEE (2017)
Video Quality Experts Group. Final report from the Video Quality Experts Group on the validation of objective models of video quality assessment, Phase II (FR TV2) (2003)
Zhai, G., Cai, J., Lin, W.: Cross-dimensional perceptual quality assessment for low bit-rate videos. IEEE Trans. Multimed. 10(7), 1316–1324 (2008)
Gu, K., Zhai, G., Yang, X.: Using Free energy principle for blind image quality assessment. IEEE Trans. Multimed. 17(1), 50–63 (2014)
Zhai, G., Cai, J., Lin, W.: Three dimensional scalable video adaptation via user-end perceptual quality assessment. IEEE Trans. Broadcast. 54(3), 719–727 (2008)
Min, X., Ma, K., Ke, G.: Unified blind quality assessment of compressed natural, graphic and screen content images. IEEE Trans. Image Process. 26, 5462–5474 (2017)
Min, X., Gu, K., Zhai, G.: Blind quality assessment based on pseudo reference image. IEEE Trans. Multimed. 20, 2049–2062 (2017)
Min, X., Zhai, G., Gu, K.: Blind image quality estimation via distortion aggravation. IEEE Trans. Broadcast. PP(99), 1–10 (2018)
Zhai, G., Wu, X., Yang, X.: A psychovisual quality metric in free-energy principle. IEEE Trans. Image Process. 21(1), 41–52 (2012)
Acknowledgement
This work was supported by the National Natural Science Foundation of China under Grant 61521062, and Grant 61527804.
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Fan, Y., Zhu, Y., Zhai, G., Wang, J., Liu, J. (2018). A Data-Driven No-Reference Image Quality Assessment via Deep Convolutional Neural Networks. In: Hong, R., Cheng, WH., Yamasaki, T., Wang, M., Ngo, CW. (eds) Advances in Multimedia Information Processing – PCM 2018. PCM 2018. Lecture Notes in Computer Science(), vol 11165. Springer, Cham. https://doi.org/10.1007/978-3-030-00767-6_34
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