Article Sidebar
Main Article Content
Image quality assessment is a crucial task in various fields such as digital photography, online content creation, and automated quality control, as it ensures an optimal visual experience and aids in maintaining consistent standards. In this paper, we propose an efficient method for training image quality assessment models on the KonIQ-10k dataset. Our novel approach utilizes a dual-Xception architecture that analyzes both the image content and additional image parameters, outperforming traditional single convolutional models. We introduce cross-sampling methods with random draw sampling of instances from majority classes, effectively enhancing prediction quality in the Mean Opinion Score (MOS) ranges that are underrepresented in the database. This methodology allows us to achieve near state-of-the-art results with limited computing costs and resources. Most importantly, our predictions across the entire spectrum of MOS values maintain consistent quality. Because of using a novel and highly effective method for image sampling, we achieved these results with much lower computational cost, making our approach the most effective way of MOS estimation on the KonIQ-10k database.
Article Details
N. Burningham, Z. Pizlo, and J. P. Allebach. Image Quality Metrics. In Hornak, Joseph P. (ed.). Encyclopedia of imaging science and technology, Wiley, New York, 2002. https://doi.org/10.1002/0471443395.img038. (Crossref)
I. H. AL-Qinani. A Review Paper on Image Quality Assessment Techniques. International Journal of Modern Trends in Engineering & Research, 6(8):1-7, 2019. https://doi.org/10.21884/IJMTER.2019.6023.SVDQQ.
S. Bosse, D. Maniry, T. Wiegand, and W. Samek. A deep neural network for image quality assessment. Proc. IEEE International Conference on Image Processing (ICIP), 2016, pp. 3773–3777. https://doi.org/10.1109/ICIP.2016.7533065. (Crossref)
L. Kang, P. Ye, Y. Li, and D. Doermann, Convolutional neural networks for no-reference image quality assessment. Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1733–1740, 2014. https://doi.org/10.1109/CVPR.2014.224. (Crossref)
S. Bianco, L. Celona, P. Napoletano, and R. Schettini. On the use of deep learning for blind image quality assessment. Signal, Image and Video Processing, 12(2)355-362, 2018. https://doi.org/10.1007/s11760-017-1166-8. (Crossref)
V. Hosu, H. Lin, T. Sziranyi, and D. Saupe. KonIQ-10k: An ecologically valid database for deep learning of blind image quality assessment. IEEE Transactions on Image Processing, 29:4041-40561, 2020. https://doi.org/10.1109/tip.2020.2967829. (Crossref)
V. R. Dendi, C. Dev, N. Kothari, and S. S. Channappayya. Generating image distortion maps using convolutional autoencoders with application to no reference image quality assessment. IEEE Signal Processing Letters, 26(1):89-93, 2018. doi: 10.1109/LSP.2018.2879518. (Crossref)
W. Zhang, K. Ma, G. Zhai and and X. Yang. Learning to blindly assess image quality in the laboratory and wild. Proc. 2020 IEEE International Conference on Image Processing (ICIP), pp. 111-115, 2020. https://doi.org/10.1109/ICIP40778.2020.9191278. (Crossref)
D. Varga, T. Szirányi, and D. Saupe. DeepRN: A content preserving deep architecture for blind image quality assessment. Proc. 2018 IEEE International Conference on Multimedia and Expo (ICME), pp. 1-6, 2018. https://doi.org/10.1109/ICME.2018.8486528. (Crossref)
D. Ghadiyaram and A. C. Bovik. Massive online crowdsourced study of subjective and objective picture quality. IEEE Transactions on Image Processing, 25(1):372-387, 2016. https://doi.org/10.1109/TIP.2015.2500021. (Crossref)
F. Chollet, Xception: Deep learning with depthwise separable convolutions. Proc. 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1800-1807, 2017. https://doi.org/10.1109/CVPR.2017.195. (Crossref)
F. Chollet, Xception: Deep learning with depthwise separable convolutions. arXiv, preprint arXiv:1610.02357, 2017. https://doi.org/10.48550/arXiv.1610.02357. (Crossref)
R. Mohammed, J. Rawashdeh and M. Abdullah. Machine learning with oversampling and undersampling techniques: Overview study and experimental results. Proc. 2020 11th International Conference on Information and Communication Systems (ICICS), pp. 243-248, 2020. https://doi.org/10.1109/ICICS49469.2020.239556. (Crossref)
D. Ghadiyaram and A. C. Bovik. Massive online crowdsourced study of subjective and objective picture quality. IEEE Transactions on Image Processing, 25(1)372-387, 2016. https://doi.org/10.1109/TIP.2015.2500021. (Crossref)
F. Gao, J. Yu, S. Zhu, Q. Huang, and Q. Tian. Blind image quality prediction by exploiting multi-level deep representations. Pattern Recognition, 81:432-442, 2018. https://doi.org/10.1016/j.patcog.2018.04.016. (Crossref)
W.-H. Kim, et al. Pixel-by-pixel Mean Opinion Score (pMOS) for no-reference image quality assessment. ArXiv, preprint arXiv:2206.06541, 2022. https://doi.org/10.48550/arXiv.2206.06541.
M. Prabhushankar, D. Temel, and G. AlRegib. MS-UNIQUE: Multi-model and sharpness-weighted unsupervised image quality estimation. Electronic Imaging, 29(12):30-35:art00006, 2017. https://doi.org/10.2352/ISSN.2470-1173.2017.12.IQSP-223. (Crossref)
X. Liu, J. van de Weijer, and A. D. Bagdanov. RankIQA: Learning from rankings for no-reference image quality assessment. Proc. IEEE International Conference on Computer Vision (ICCV), pp. 1040–1049, 2017. https://doi.org/10.1109/ICCV.2017.118. (Crossref)
K. Ma, W. Liu, K. Zhang, Z. Duanmu, Z. Wang, and W. Zuo. End-to-end blind image quality assessment using deep neural networks. IEEE Transactions on Image Processing, 27(3):1202-1213, 2018. https://doi.org/10.1109/TIP.2017.2774045. (Crossref)
H. Talebi and P. Milanfar, NIMA: Neural image assessment. IEEE Transactions on Image Processing, 27(8):3998-4011, 2018. https://doi.org/10.1109/TIP.2018.2831899. (Crossref)
C. Szegedy, S. Ioffe, V. Vanhoucke, and A. Alemi. Inception-v4, Inception-ResNet and the impact of residual connections on learning. Proc. 31st AAAI Conference on Artificial Intelligence, Vol. 31, No. 1, pp. 4278-4284, 2017. https://doi.org/10.1609/aaai.v31i1.11231. (Crossref)
K. He, X. Zhang, S. Ren, and J. Sun. Deep residual learning for image recognition. Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 770-778, 2016. https://doi.org/10.1109/CVPR.2016.90. (Crossref)
J. Hu, L. Shen, and G. Sun. Squeeze-and-Excitation Networks. Proc. IEEE Conference on Computer Vision and Pattern Recognition, pp. 7132-7141, 2018. https://doi.org/10.1109/CVPR.2018.00745. (Crossref)
J. Deng, W. Dong, Socher, R., Li-Jia Li, Kai Li, Li Fei-Fei. ImageNet: A large-scale hierarchical image database. Proc. 2009 IEEE Conference on Computer Vision and Pattern Recognition, pp. 248-255, 2009. https://doi.org/10.1109/cvprw.2009.5206848. (Crossref)
D. P. Kingma, J. Ba. Adam: A method for stochastic optimization. Proc. 3rd International Conference on Learning Representations (ICLR), San Diego, CA, USA, May 7-9, 2015. https://doi.org/https://arxiv.org/abs/1412.6980.
A. Moorthy and A. Bovik. A two-step framework for constructing blind image quality indices. IEEE Signal Processing Letters, 17(5):513-516, 2010. https://doi.org/10.1109/LSP.2010.2043888. (Crossref)
M. A. Saad, A. C. Bovik, and C. Charrier. Blind image quality assessment: A natural scene statistics approach in the DCT domain. IEEE Transactions on Image Processing, 21(8):3339-3352, 2012. https://doi.org/10.1109/TIP.2012.2191563. (Crossref)
A. Mittal, A. K. Moorthy, and A. C. Bovik. No-reference image quality assessment in the spatial domain. IEEE Transactions on Image Processing, 21(12):4695-4708, 2012. https://doi.org/10.1109/TIP.2012.2214050. (Crossref)