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No-Reference Image Quality Assessment via Broad Learning System

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Image and Graphics (ICIG 2021)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 12890))

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Abstract

Deep Learning (DL) can be used to model the process of No Reference-Image Quality Assessment (NR-IQA), which has a great contribution to the field of image processing. Even though, a large number of super parameters make the computational complexity gradually increase. Surprisingly, Broad Learning System (BLS) can transform the deep structure of DL into a flat and visual network structure, which reduces the difficulty for practical applications. By applying BLS in NR-IQA, combining the structural and statistical features of the image to reflect the image quality, which expands the research of NR-IQA undoubtedly. In this paper, the mathematical relationship between the image and the score is modeled by BLS, the effectiveness of the proposed method is demonstrated in the numerical experiments.

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References

  1. Liu, L., Hua, Y., Zhao, Q., Huang, H, Bovik, A.C.: Blind image quality assessment by relative gradient statistics and AdaBoosting neural network[J]. Signal Process Image Commun. 40(C), 1–15 (2016)

    Google Scholar 

  2. Bianco, S., Celona, L., Napoletano, P., Schettini, R.: On the use of deep learning for blind image quality assessment[J]. SIViP 12(2), 355–362 (2018)

    Article  Google Scholar 

  3. Gao, F., Yu, J.: Blind image quality prediction by exploiting multi-level deep representations[J]. Pattern Recogn. 81, 432–442 (2018)

    Article  Google Scholar 

  4. Fang, Y., Zhang, C., Yang, W., Liu, J., Guo, Z.: Blind visual quality assessment for image super-resolution by convolutional neural network[J]. Multimedia Tools Appl. 77(22), 29829–29846 (2018)

    Article  Google Scholar 

  5. Fang, Y.M., Yan, J.B.: Stereoscopic image quality assessment by deep convolutional neural network[J]. J. Vis. Commun. Image Represent. 58, 400–406 (2019)

    Article  Google Scholar 

  6. Jia, S., Zhang, Y.: Saliency-based deep convolutional neural network for no-reference image quality assessment[J]. Multimedia Tools Appl. 77(12), 14859–14872 (2018)

    Article  Google Scholar 

  7. Yang, J.C., Zhao, Y., Zhu, Y.H., Xua, H.F., Lu, W., Meng, Q.G.: Blind assessment for stereo images considering binocular characteristics and deep perception map based on deep belief network[J]. Inf. Sci. 474, 1–17 (2019)

    Article  Google Scholar 

  8. Ji, W.P., Wu, J.J., Shi, G.M., Wan, W.F., Xie, X.M.: Blind image quality assessment with semantic information[J]. J. Vis. Commun. Image Represent 58, 195–204 (2019)

    Article  Google Scholar 

  9. Wu, J., Zeng, J., Liu, Y., Shi, G.M.: Hierarchical feature degradation based blind image quality assessment[C]. In: IEEE International Conference on Computer Vision Workshop (2017)

    Google Scholar 

  10. Gao, F., Wang, Y., Li, P.P., Tan, M., Yu, J., Zhu, Y.: DeepSim: deep similarity for image quality assessment [J]. Neuro comput. 257, 104–114 (2017)

    Google Scholar 

  11. Yang, Y., Cheng, G., Yu, D.H., Ye, R.Z.: Blind image quality assessment via content-invariant statistical feature[J]. Optik 138, 21–32 (2017)

    Article  Google Scholar 

  12. Lin, K.Y., Wang, G.: Hallucinated-IQA: no-reference image quality assessment via adversarial learning[J]. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 732–741 (2018)

    Google Scholar 

  13. Ren, H., Chen, D., Wang, Y.: RAN4IQA: Restorative adversarial nets for no-reference image quality assessment[C]. arXiv preprint arXiv:1712.05444 (2017)

  14. Lim, H.T., Kim, H.G., Ro, Y.M.: VR IQA NET: deep virtual reality image quality assessment using adversarial learning[C]. In: IEEE International Conference on Acoustics Speech and Signal Processing (ICASSP), pp. 6737–6741 (2018)

    Google Scholar 

  15. Pan, D., Shi, P., Hou, M., Ying, Z.F., Fu, S.Z., Zhang, Y.: Blind predicting similar quality map for image quality assessment[C]. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 6373–6382 (2018)

    Google Scholar 

  16. Hinton, G.E., Osindero, S., Teh, Y.W.: A fast learning algorithm for deep belief nets[J]. Neural Comput. 18(7), 1527–1554 (2006)

    Article  MathSciNet  Google Scholar 

  17. Hinton, G.E., Sejnowski, T.J.: Learning and relearning in Boltzmann Machines[J]. Parallel Distrib. Process. Explor. Microstruct. Cogn. 1, 282–317 (1986)

    Google Scholar 

  18. Pao, Y.H., Takefuji, Y.: Functional-link net computing: theory, system architecture, and functionalities[J]. Computer 25(5), 76–79 (1992)

    Article  Google Scholar 

  19. Pao, Y.H., Park, G.H., Sobajic, D.J.: Learning and generalization characteristics of the random vector functional-link net[J]. Neurocomputing 6(2), 163–180 (1994)

    Article  Google Scholar 

  20. Igelnik, B., Pao, Y.H.: Stochastic choice of basis functions in adaptive function approximation and the functional-link net[J]. IEEE Trans. Neural Netw. Learn. Syst. 6(6), 1320–1329 (1995)

    Article  Google Scholar 

  21. Chen, C.L.P., Wan, J.Z.: A rapid learning and dynamic stepwise updating algorithm for flat neural networks and the application to time-series prediction[J]. IEEE Trans. Syst. Man Cybern. Part B (Cybernetics) 29(1), 62–72 (1999)

    Google Scholar 

  22. Chen, C.L.P., Liu, Z.: Broad learning system: an effective and efficient incremental learning system without the need for deep architecture[J]. IEEE Trans. Neural Netw. Learn. Syst. 29(1), 10–24 (2018)

    Article  MathSciNet  Google Scholar 

  23. Shuang, F., Chen, C.L.P.: Fuzzy broad learning system: a novel neuro-fuzzy model for regression and classification[J]. IEEE Trans. Cybern., 1–11 (2018)

    Google Scholar 

  24. Xue, W., Mou, X., Zhang, L., Bovik, A.C., Feng, X.C., et al.: Blind image quality assessment using joint statistics of gradient magnitude and Laplacian features[J]. IEEE Trans. Image Process. 23(11), 4850–4862 (2014)

    Article  MathSciNet  Google Scholar 

  25. Geusebroek, J.M., Smeulders, A.: A six-stimulus theory for stochastic texture[J]. Int. J. Comput. Vision 62(1–2), 7–16 (2005)

    Article  Google Scholar 

  26. Huang, J.G., Mumford, D.: Statistics of natural images and models[C]. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition (1999)

    Google Scholar 

  27. Ruderman, D.L.: The statistics of natural images[J]. Netw. Comput. Neural Syst. 5(4), 517–548 (1994)

    Google Scholar 

  28. Sheikh, H.R., Wang, Z., Cormack, L., Bovik, A.C.: LIVE image quality assessment database release 2. http://live.ece.utexas.edu/research/quality. 4 February 2015

  29. Larson, E.C., Chandler, D.M.: Most apparent distortion: full reference image quality assessment and the role of strategy[J]. J. Electron. Imaging 19(011006), 1–21 (2010)

    Google Scholar 

  30. Ponomarenko, N., Lukin, V., Zelensky, A.: TID2008 a database for evaluation of full-reference visual quality assessment metrics[J]. Adv. Modern Radio Electr. 1(10), 30–45 (2009)

    Google Scholar 

  31. Ponomarenko, N., Jin, L., Leremeiev, O., Lukin, V., Egiazarian, K., Astola, J., et al.: Image database TID2013: peculiarities, results and perspectives[J]. Signal Process. Image Commun. 1(30), 55–77 (2015)

    Google Scholar 

  32. Wang, Z., Bovik, A.C., Lu, L.G.: Why is image quality assessment so difficult?[C]. In: IEEE International Conference on Acoustics, Speech, and Signal Processing, pp. 3313–3316 (2002)

    Google Scholar 

  33. Wang, Z., Bovik, A.C., Sheikh, H.R., Simoncelli, E.P.: Image quality assessment: from error visibility to structural similarity[J]. IEEE Trans. Image Process. 13(4), 600–612 (2004)

    Article  Google Scholar 

  34. Zhang, L., Zhang, L., Mou, X.Q., Zhang, D.: FSIM: a feature similarity index for image quality assessment[J]. IEEE Trans. Image Process. 20(8), 2378–2386 (2011)

    Article  MathSciNet  Google Scholar 

  35. Moorthy, A.K., Bovik, A.C.: A two-step framework for constructing blind image quality indices[J]. IEEE Signal Process. Lett. 17(5), 513–516 (2010)

    Article  Google Scholar 

  36. Moorthy, A.K., Bovik, A.C.: Blind image quality assessment: from natural scene statistics to perceptual quality[J]. IEEE Trans. Image Process. 20(12), 3350–3364 (2011)

    Article  MathSciNet  Google Scholar 

  37. Saad, M.A., Bovik, A.C., Charrier, C.: Blind image quality assessment: a natural scene statistics approach in the DCT domain[J]. IEEE Trans. Image Process. 21(8), 3339–3352 (2012)

    Article  MathSciNet  Google Scholar 

  38. Ye, P., Kumar, J., Kang, L., Doermann, D.: Unsupervised feature learning framework for no-reference image quality assessment[C]. In: IEEE Conference on Computer Vision and Pattern Recognition (2012)

    Google Scholar 

Download references

Acknowledgments

This work was supported in part by the National Natural Science Foundation of China (Grant No. 62061040, 51769026); the Major Innovation Projects for Building First-class Universities in China’s Western Region (Grant No. ZKZD2017009); the Natural Science Foundation of Ningxia (Grant No. 2018AAC03014) and the Postgraduate Innovation Project of Ningxia University ( No. GIP2019011).

Author information

Authors and Affiliations

  1. School of Mathematics and Statistics, Ningxia University, Yinchuan, China

    Jing Yue, Guojun liu & Lizhuan Huang

  2. School of Electrical and Control Engineering, Xi’an University of Science and Technology, Xi’an, China

    Jing Yue

Authors
  1. Jing Yue
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  2. Guojun liu
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  3. Lizhuan Huang
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Corresponding author

Correspondence to Guojun liu .

Editor information

Editors and Affiliations

  1. Peking University, Beijing, China

    Yuxin Peng

  2. Tsinghua University, Beijing, China

    Shi-Min Hu

  3. Tampere University, Tampere, Finland

    Moncef Gabbouj

  4. Zhejiang University, Hangzhou, China

    Kun Zhou

  5. Technion – Israel Institute of Technology, Haifa, Israel

    Michael Elad

  6. Tsinghua University, Beijing, China

    Kun Xu

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Yue, J., liu, G., Huang, L. (2021). No-Reference Image Quality Assessment via Broad Learning System. In: Peng, Y., Hu, SM., Gabbouj, M., Zhou, K., Elad, M., Xu, K. (eds) Image and Graphics. ICIG 2021. Lecture Notes in Computer Science(), vol 12890. Springer, Cham. https://doi.org/10.1007/978-3-030-87361-5_14

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  • DOI: https://doi.org/10.1007/978-3-030-87361-5_14

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  • Publisher Name: Springer, Cham

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  • Online ISBN: 978-3-030-87361-5

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