Abstract
Convolutional neural networks (CNNs)-based no-reference image quality assessment (NR-IQA) suffers from insufficient training data. The conventional solution is splitting the training image into patches, assigning each patch the quality score, while the assignment of patch score is not consistent with the human visual system (HVS) well. To address the problem, we propose a patch quality assignment strategy, introducing the weighting map to describe the degree of visual importance of each distorted pixel, integrating the weighting map and the feature map to pool the quality score of each patch. With the patch quality, a CNNs-based NR-IQA model is trained. Experimental results demonstrate that proposed method, named as blind image quality metric with improved patch score (BIQIPS), improves the performance on most of the distortion types, especially on the types of local distortions, and achieves state-of-the-art prediction accuracy among the NR-IQA metrics.
Supported by National Nature Science Foundation of China (No. 61572058) and National Key R&D Program of China (No. 2017YFB1002702).
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Wu, H.R., Rao, K.R.: Digital Video Image Quality and Perceptual Coding. CRC Press, Boca Raton (2017)
Ahmad, R., Halsall, F., Zhang, J.S.: The transmission of compressed video over ATM networks. In: Teletraffic Symposium, 11th Performance Engineering in Telecommunications Networks, pp. 1–20. IEE Eleventh UK, IET (1994)
Nakayama, Y., et al.: Abdominal CT with low tube voltage: preliminary observations about radiation dose, contrast enhancement, image quality, and noise. Radiology 237, 945–951 (2005)
Kang, L., Ye, P., Li, Y., Doermann, D.: Convolutional neural networks for no-reference image quality assessment. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1733–1740 (2014)
Heng, W., Jiang, T.: From image quality to patch quality: an image-patch model for no-reference image quality assessment. In: 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1238–1242. IEEE (2017)
Narvekar, N.D., Karam, L.J.: A no-reference perceptual image sharpness metric based on a cumulative probability of blur detection. In: 2009 International Workshop on Quality of Multimedia Experience, QoMEx 2009, pp. 87–91. IEEE (2009)
Moorthy, A.K., Bovik, A.C.: A two-step framework for constructing blind image quality indices. IEEE Signal Process. Lett. 17, 513–516 (2010)
Chandler, D.M.: Seven challenges in image quality assessment: past, present, and future research. ISRN Signal Process. 2013, 53 (2013)
Mittal, A., Moorthy, A.K., Bovik, A.C.: No-reference image quality assessment in the spatial domain. IEEE Trans. Image Process. 21, 4695–4708 (2012)
Ye, P., Kumar, J., Kang, L., Doermann, D.: Unsupervised feature learning framework for no-reference image quality assessment. In: 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1098–1105. IEEE (2012)
Zhang, L., Gu, Z., Liu, X., Li, H., Lu, J.: Training quality-aware filters for no-reference image quality assessment. IEEE Multimedia 21, 67–75 (2014)
Wang, H., Zuo, L., Fu, J.: Distortion recognition for image quality assessment with convolutional neural network. In: 2016 IEEE International Conference on Multimedia and Expo (ICME), pp. 1–6. IEEE (2016)
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, 206–220 (2017)
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, 206–219 (2018)
Zhang, L., Shen, Y., Li, H.: VSI: a visual saliency-induced index for perceptual image quality assessment. IEEE Trans. Image Process. 23, 4270–4281 (2014)
Kim, J., Lee, S.: Deep learning of human visual sensitivity in image quality assessment framework. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2017)
Wang, Z., Bovik, A.C., Sheikh, H.R., Simoncelli, E.P.: Image quality assessment: from error visibility to structural similarity. IEEE Trans. Image Process. 13, 600–612 (2004)
Nair, V., Hinton, G.E.: Rectified linear units improve restricted Boltzmann machines. In: Proceedings of the 27th International Conference on Machine Learning (ICML 2010), pp. 807–814 (2010)
Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014)
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)
Jia, Y., et al.: Caffe: convolutional architecture for fast feature embedding. In: Proceedings of the 22nd ACM international conference on Multimedia, pp. 675–678. ACM (2014)
Sheikh, H.R., Wang, Z., Cormack, L., Bovik, A.C.: Live image quality assessment database release 2 (2005, 2016)
Ponomarenko, N., Lukin, V., Zelensky, A., Egiazarian, K., Carli, M., Battisti, F.: Tid 2008-a database for evaluation of full-reference visual quality assessment metrics. Adv. Mod. Radioelectron. 10, 30–45 (2009)
Zhang, L., Zhang, L., Mou, X., Zhang, D.: Fsim: A feature similarity index for image quality assessment. IEEE Trans. Image Process. 20, 2378–2386 (2011)
Mittal, A., Soundararajan, R., Bovik, A.C.: Making a completely blind image quality analyzer. IEEE Signal Process. Lett. 20, 209–212 (2013)
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. Computer Vision and Pattern Recognition, https://arxiv.org/abs/1707.08347v1 (2017)
Xu, J., Ye, P., Li, Q., Du, H., Liu, Y., Doermann, D.: Blind image quality assessment based on high order statistics aggregation. IEEE Trans. Image Process. 25, 4444–4457 (2016)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Lv, Z., Wang, X., Wang, K., Liang, X. (2019). A Deep Blind Image Quality Assessment with Visual Importance Based Patch Score. In: Jawahar, C., Li, H., Mori, G., Schindler, K. (eds) Computer Vision – ACCV 2018. ACCV 2018. Lecture Notes in Computer Science(), vol 11362. Springer, Cham. https://doi.org/10.1007/978-3-030-20890-5_10
Download citation
DOI: https://doi.org/10.1007/978-3-030-20890-5_10
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-20889-9
Online ISBN: 978-3-030-20890-5
eBook Packages: Computer ScienceComputer Science (R0)