Blind Panoramic Image Quality Assessment Based on Project-Weighted Local Binary Pattern

  • Yumeng Xia
  • Yongfang WangEmail author
  • Peng Ye
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 1181)


The majority of existing objective panoramic image quality assessment algorithms are based on peak signal to noise ratio (PSNR) or structural similarity (SSIM). However, they are not highly consistent with human perception. In this paper, a new blind panoramic image quality assessment metric is proposed based on project-weighted gradient local binary pattern histogram (PWGLBP), which explores the structure degradation in sphere by combining with the nonlinear transformation relationship between the projected plane and sphere. Finally, support vector regression (SVR) is adopted to learn a quality predictor from feature space to quality score space. The experimental results demonstrate the superiority of our proposed metric compared with state-of-the-art objective PIQA methods.


Panoramic image Blind quality assessment Local binary pattern Nonlinear transformation Support vector regression 



This work was supported by Natural Science Foundation of China under Grant No. 61671283, 61301113.


  1. 1.
    Battisti, F., Carli, M., Le Callet, P., Paudyal, P.: Toward the assessment of quality of experience for asymmetric encoding in immersive media. IEEE Trans. Broadcast. 64(2), 392–406 (2018)CrossRefGoogle Scholar
  2. 2.
    Yang, S., Zhao, J., Jiang, T.: An objective assessment method based on multi-level factors for panoramic videos. In: IEEE Visual Communications and Image Processing, pp. 1–4 (2017)Google Scholar
  3. 3.
    Greene, N.: Environment mapping and other applications of world projections. IEEE Comput. Appl. 6(11), 21–29 (1986)CrossRefGoogle Scholar
  4. 4.
    Choi, K.P., Zakharchenko, V.: Test sequence formats for virtual reality video coding. In: JVET-C0050, 3rd JVET Meeting, Geneva, CH, June 2016Google Scholar
  5. 5.
    Li, J., Wen, Z., Li, S.: Novel tile segmentation scheme for omnidirectional video. In: Proceedings of the 2016 IEEE International Conference on Image Processing, pp. 370–374 (2016)Google Scholar
  6. 6.
    Yu, M., Lakshman, H., Girod, B.: A framework to evaluate omnidirectional video coding schemes. In: Proceedings of the 2015 IEEE International Symposium Mixed Augmented Reality, pp. 31–36 (2015)Google Scholar
  7. 7.
    Zakharchenko, V., Choi, K.P., Park, J.H.: Omnidirectional video quality metrics and evaluation process. In: Data Compression Conference, p. 472 (2017)Google Scholar
  8. 8.
    Sun, Y., Lu, A., Yu, L.: Weighted-to-spherically uniform quality evaluation for omnidirectional video. IEEE Signal Process. Lett. 24(9), 1408–1412 (2017)Google Scholar
  9. 9.
    Chen, S., Zhang, Y., Li, Y., Chen, Z., Wang, Z.: Spherical structural similarity index for objective omnidirectional video quality assessment. In: IEEE International Conference on Multimedia and Expo (ICME), San Diego, CA, pp. 1–6 (2018)Google Scholar
  10. 10.
    Hore, A., Ziou, D.: Image quality metrics: PSNR vs. SSIM. In: 20th International Conference on Pattern Recognition, Istanbul, pp. 2366–2369 (2010)Google Scholar
  11. 11.
    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)CrossRefGoogle Scholar
  12. 12.
    Duan, H., Zhai, G., Min, X., Zhu, Y., Fang, Y., Yang, X.: Perceptual quality assessment of omnidirectional images. In: IEEE International Symposium on Circuits and Systems (ISCAS), Florence, pp. 1–5 (2018)Google Scholar
  13. 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)MathSciNetCrossRefGoogle Scholar
  14. 14.
    Li, Q., Lin, W., Fang, Y.: No-reference quality assessment for multiply-distorted images in gradient domain. IEEE Signal Process. Lett. 23(4), 541–545 (2016)CrossRefGoogle Scholar
  15. 15.
    Gu, K., Li, L., Lu, H., Min, X., Lin, W.: A fast reliable image quality predictor by fusing micro- and macro-structures. IEEE Trans. Industr. Electron. 64(5), 3903–3912 (2017)CrossRefGoogle Scholar
  16. 16.
    Zou, W., Yang, F., Wan, S.: Perceptual video quality metric for compression artifacts: from two-dimensional to omnidirectional. IET Image Proc. 12(3), 374–381 (2018)CrossRefGoogle Scholar
  17. 17.
    Chang, C.C., Lin, C.J.: LIBSVM: a library for support vector machines. ACM Trans. Intell. Syst. Technol. 2(3), 1–27 (2011)CrossRefGoogle Scholar
  18. 18.
    Xu, M., Li, C., Liu, Y., Deng, X., Lu, J.: A subjective visual quality assessment method of panoramic videos. In: IEEE International Conference on Multimedia and Expo (ICME), Hong Kong, pp. 517–522 (2017)Google Scholar
  19. 19.
    Zhang, B., Gao, Y., Wang, Y.: Local derivative pattern versus local binary pattern: face recognition with high-order local binary pattern descriptor. IEEE Trans. Image Process. 19(2), 533–544 (2010)MathSciNetCrossRefGoogle Scholar
  20. 20.
    Ojala, T., Pietikainen, M., Maenpaa, T.: Multiresolution gray-scale and rotation invariant texture classification with local binary pattern. IEEE Trans. Pattern Anal. Mach. Intell. 24(7), 971–987 (2002)CrossRefGoogle Scholar
  21. 21.
    Wu, J., Lin, W., Shi, G., Liu, A.: Perceptual quality metric with internal generative mechanism. IEEE Trans. Image Process. 22(1), 43–54 (2013)MathSciNetCrossRefGoogle Scholar

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© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  1. 1.Shanghai Institute for Advanced Communication and Data ScienceShanghai UniversityShanghaiChina
  2. 2.School of Communication and Information EngineeringShanghai UniversityShanghaiChina

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