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No-Reference Image Quality Assessment via Multi-order Perception Similarity

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Pattern Recognition and Computer Vision (PRCV 2019)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 11858))

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Abstract

No-reference image quality assessment (NR-IQA) aims to develop models that can predict the quality of distorted image automatically and accurately without the reference. Lack of reference makes NR-IQA based on feature learning difficult to avoid the impact of image contents on features. In this paper, we follow an innovative strategy and present a novel NR-IQA approach based on multi-order perception similarity to overcome the difficulty. The key to our strategy is that the high and low order feature maps can be the reference to each other to reduce the dependence of features on image content. In our framework, the similarity relies on describing the retention of fine structures that is extremely sensitive to distortion in distorted image to predict quality degradation, which is monotonic with respect to the distortion level. The similarities of features from structure to texture between high and low order feature maps are utilized to predict image quality. Finally, a regression model is trained to learn the map** from feature similarities to perceptual quality. Extensive experiments demonstrate the effectiveness and superiority of our approach against compared methods.

The first author is a postgraduate with email addresses: zhzhou_1@stu.xidian.edu.cn. This research was supported in part by the National Natural Science Foundation of China (Grant Nos. 61432014, 61871311, 61876146), the National Key Research and Development Program of China (Grant No. 2016QY01W0200) and the Key Industrial Innovation Chain Project in Industrial Domain (Grant No. 2016KTZDGY04-02).

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References

  1. Cai, H., Li, L., Yi, Z., Gong, M.: Towards a blind image quality evaluator using multi-scale second-order statistics. Sig. Process. Image Commun. 71, 88–99 (2019)

    Article  Google Scholar 

  2. Freitas, P., Akamine, W., Farias, M.: No-reference image quality assessment using orthogonal color planes patterns. IEEE Trans. Multimedia 20(12), 3353–3360 (2018)

    Article  Google Scholar 

  3. Ghadiyaram, D., Bovik, A.C.: Massive online crowdsourced study of subjective and objective picture quality. IEEE Trans. Image Process. 25(1), 372–387 (2015)

    Article  MathSciNet  MATH  Google Scholar 

  4. Ghadiyaram, D., Bovik, A.C.: Perceptual quality prediction on authentically distorted images using a bag of features approach. J. Vis. 17(1), 1–29 (2017)

    Article  Google Scholar 

  5. Gu, K., Li, L., Lu, H., Min, X., Lin, W.: A fast reliable image quality predictor by fusing micro- and macro-structures. IEEE Trans. Ind. Electron. 64(5), 3903–3912 (2017)

    Article  Google Scholar 

  6. Ren, H., Wang, D.C.Y.: RAN4IQA: restorative adversarial nets for no-reference image quality assessment. In: Thirty-Second AAAI Conference on Artificial Intelligence (2018)

    Google Scholar 

  7. Jayaraman, D., Mittal, A., Moorthy, A.K., Bovik, A.C.: Objective quality assessment of multiply distorted images. In: 2012 Conference Record of the Forty Sixth Asilomar Conference on Signals, Systems and Computers (ASILOMAR), pp. 1693–1697 (2012)

    Google Scholar 

  8. Kang, L., Ye, P., Li, Y., Doermann, D.: Convolutional neural networks for no-reference image quality assessment. In: 2014 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1733–1740 (2014)

    Google Scholar 

  9. Kim, J., Lee, S.: Fully deep blind image quality predictor. IEEE J. Sel. Top. Sign. Process. 11(1), 206–220 (2017)

    Article  Google Scholar 

  10. Kim, J., Zeng, H., Ghadiyaram, D., Lee, S., Zhang, L., Bovik, A.C.: Deep convolutional neural models for picture-quality prediction: challenges and solutions to data-driven image quality assessment. IEEE Sig. Process. Mag. 34(6), 130–141 (2017)

    Article  Google Scholar 

  11. Larson, E., Cooper, E., Chandler, D.M.: Most apparent distortion: full-reference image quality assessment and the role of strategy. J. Electron. Imaging 19(1), 011006 (2010)

    Article  Google Scholar 

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

    Google Scholar 

  13. Liu, T., Liu, K.: No-reference image quality assessment by wide-perceptual-domain scorer ensemble method. IEEE Trans. Image Process. 27(3), 1138–1151 (2018)

    Article  MathSciNet  MATH  Google Scholar 

  14. Min, X., Gu, K., Zhai, G., Liu, J., Yang, X., Chen, C.W.: Blind quality assessment based on pseudo-reference image. IEEE Trans. Multimedia 20(8), 2049–2062 (2018)

    Article  Google Scholar 

  15. Mittal, A., Moorthy, A.K., Bovik, A.C.: No-reference image quality assessment in the spatial domain. IEEE Trans. Image Process. 21(12), 4695–4708 (2012)

    Article  MathSciNet  MATH  Google Scholar 

  16. Mittal, A., Soundararajan, R., Bovik, A.C.: Making a “completely blind” image quality analyzer. IEEE Sig. Process. Lett. 20(3), 209–212 (2013)

    Article  Google Scholar 

  17. Ojala, T., Pietikainen, M., Maenpaa, T.: Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Trans. Pattern Anal. Mach. Intell. 24(7), 971–987 (2002)

    Article  MATH  Google Scholar 

  18. Oszust, M.: Local feature descriptor and derivative filters for blind image quality assessment. IEEE Sig. Process. Lett. 26(2), 322–326 (2019)

    Article  Google Scholar 

  19. Ponomarenko, N., et al.: Color image database TID2013: Peculiarities and preliminary results. In: European Workshop on Visual Information Processing (EUVIP), pp. 106–111 (2013)

    Google Scholar 

  20. Ruderman, D.L., Cronin, T.W., Chiao, C.C.: Statistics of cone responses to natural images: implications for visual coding. J. Opt. Soc. Am. A 15(15), 2036–2045 (1998)

    Article  Google Scholar 

  21. 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), 3339–3352 (2012)

    Article  MathSciNet  MATH  Google Scholar 

  22. 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)

    Article  Google Scholar 

  23. 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(4), 600–612 (2004)

    Article  Google Scholar 

  24. 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(9), 4444–4457 (2016)

    Article  MathSciNet  MATH  Google Scholar 

  25. Xue, W., Zhang, L., Mou, X.: Learning without human scores for blind image quality assessment. In: 2013 IEEE Conference on Computer Vision and Pattern Recognition, pp. 995–1002 (2013)

    Google Scholar 

  26. Xue, W., Zhang, L., Mou, X., Bovik, A.C.: Gradient magnitude similarity deviation: a highly efficient perceptual image quality index. IEEE Trans. Image Process. 23(2), 684–695 (2014)

    Article  MathSciNet  MATH  Google Scholar 

  27. 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, pp. 1098–1105 (2012)

    Google Scholar 

  28. Zhang, L., Shen, Y., Li, H.: VSI: a visual saliency-induced index for perceptual image quality assessment. IEEE Trans. Image Process. 23(10), 4270–4281 (2014)

    Article  MathSciNet  MATH  Google Scholar 

  29. Zhang, L., Zhang, L., Bovik, A.C.: A feature-enriched completely blind image quality evaluator. IEEE Trans. Image Process. 24(8), 2579–2591 (2015)

    Article  MathSciNet  MATH  Google Scholar 

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

    Article  MathSciNet  MATH  Google Scholar 

  31. Zhang, M., Muramatsu, C., Zhou, X., Hara, T., Fujita, H.: Blind image quality assessment using the joint statistics of generalized local binary pattern. IEEE Trans. Image Process. 22(2), 207–210 (2015)

    Google Scholar 

  32. Zhou, Z., Lu, W., Yang, J., He, L., Gao, X.: Blind image quality assessment based on visuo-spatial series statistics. In: 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 3161–3165 (2018)

    Google Scholar 

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Author information

Authors and Affiliations

  1. School of Electronic Engineering, **dian University, **’an, China

    Ziheng Zhou, Wen Lu & Shishuai Han

  2. Science and Technology on Communication Networks Laboratory, **’an, China

    Ziheng Zhou

  3. School of Electronic Information Engineering, Tian** University, Tian**, China

    Jiachen Yang

Authors
  1. Ziheng Zhou
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  2. Wen Lu
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  3. Jiachen Yang
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  4. Shishuai Han
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Corresponding author

Correspondence to Ziheng Zhou .

Editor information

Editors and Affiliations

  1. School of EECS, Peking University, Bei**g, China

    Zhouchen Lin

  2. Institute of Automation, Chinese Academy of Sciences, Bei**g, China

    Liang Wang

  3. Nan**g University of Science and Technology, Nan**g, China

    Jian Yang

  4. **dian University, **'an, China

    Guangming Shi

  5. Institute of Automation, Chinese Academy of Sciences, Bei**g, China

    Tieniu Tan

  6. Institute of Artificial Intelligence, **'an Jiaotong University, **'an, China

    Nanning Zheng

  7. Chinese Academy of Sciences, Bei**g, China

    **lin Chen

  8. Northwestern Polytechnical University, **'an, China

    Yanning Zhang

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Zhou, Z., Lu, W., Yang, J., Han, S. (2019). No-Reference Image Quality Assessment via Multi-order Perception Similarity. In: Lin, Z., et al. Pattern Recognition and Computer Vision. PRCV 2019. Lecture Notes in Computer Science(), vol 11858. Springer, Cham. https://doi.org/10.1007/978-3-030-31723-2_52

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  • DOI: https://doi.org/10.1007/978-3-030-31723-2_52

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-31722-5

  • Online ISBN: 978-3-030-31723-2

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