Signal, Image and Video Processing

, Volume 10, Issue 5, pp 803–810 | Cite as

Content-weighted mean-squared error for quality assessment of compressed images

  • Ke GuEmail author
  • Shiqi Wang
  • Guangtao Zhai
  • Siwei Ma
  • Xiaokang Yang
  • Wenjun Zhang
Original Paper


Image quality assessment (IQA) has been intensively studied, especially for the full-reference (FR) scenario. However, only the mean-squared error (MSE) is widely employed in compression. Why other IQA metrics work ineffectively? We first sum up three main limitations including the computational time, portability, and working manner. To address these problems, we then in this paper propose a new content-weighted MSE (CW-MSE) method to assess the quality of compressed images. The design principle of our model is to use adaptive Gaussian convolution to estimate the influence of image content in a block-based manner, thereby to approximate the human visual perception to image quality. Results of experiments on six popular subjective image quality databases (including LIVE, TID2008, CSIQ, IVC, Toyama and TID2013) confirm the superiority of our CW-MSE over state-of-the-art FR IQA approaches.


Image quality assessment (IQA) Mean-squared error (MSE) Image compression Image content 


  1. 1.
    Sheikh, H.R., Wang, Z., Cormack, L., Bovik, A.C.: LIVE image quality assessment database release 2.
  2. 2.
    Ponomarenko, N., Lukin, V., Zelensky, A., Egiazarian, K., Carli, M., Battisti, F.: TID2008—a database for evaluation of full-reference visual quality assessment metrics. Adv. Mod. Radioelectron. 10, 30–45 (2009)Google Scholar
  3. 3.
    Larson, E.C., Chandler, D.M.: Categorical image quality (CSIQ) database.
  4. 4.
    Ninassi, A., Le Callet, P., Autrusseau, F.: Subjective quality assessment-IVC database.
  5. 5.
    Horita, Y., Shibata, K., Kawayoke, Y., Sazzad, Z.M.P.: MICT image quality evaluation database.
  6. 6.
    Ponomarenko, N., Ieremeiev, O., Lukin, V., Egiazarian, K., Jin, L., Astola, J., Vozel, B., Chehdi, K., Carli, M., Battisti, F., Jay Kuo, C.-C.: Color image database TID2013: peculiarities and preliminary results. In: EUVIP2013, pp. 106–111, Jun 2013Google Scholar
  7. 7.
    Rehman, A., Wang, Z.: SSIM-based nonlocal means image denoising. In: Proceedings of the IEEE International Conference on Image Processing, pp. 217–220, Sept 2011Google Scholar
  8. 8.
    Rehman, A., Wang, Z.: Reduced-reference image quality assessment by structural similarity estimation. IEEE Trans. Image Process. 21(8), 3378–3389 (2012)MathSciNetCrossRefGoogle Scholar
  9. 9.
    Wang, S., Rehman, A., Wang, Z., Ma, S., Gao, W.: SSIM-motivated rate distortion optimization for video coding. IEEE Trans. Circuits Syst. Video Technol. 22(4), 516–529 (2012)MathSciNetCrossRefGoogle Scholar
  10. 10.
    Wang, S., Rehman, A., Wang, Z., Ma, S., Gao, W.: Perceptual video coding based on SSIM-inspired divisive normalization. IEEE Trans. Image Process. 22(4), 1418–1429 (2013)MathSciNetCrossRefGoogle Scholar
  11. 11.
    Zhao, T., Zeng, K., Rehman, A., Wang, Z.: On the use of SSIM in HEVC. In: Proceedings of the IEEE Asilomar Conference on Signals, Systems and Computers, pp. 1107–1111, Nov 2013Google Scholar
  12. 12.
    Yeganeh, H., Wang, Z.: Objective quality assessment of tone-mapped images. IEEE Trans. Image Process. 22(2), 657–667 (2013)MathSciNetCrossRefGoogle Scholar
  13. 13.
    Yeganeh, H., Wang, Z.: High dynamic range image tone mapping by maximizing a structural fidelity measure. In: Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 1879–1883, May 2013Google Scholar
  14. 14.
    Gu, K., Zhai, G., Liu, M., Yang, X., Zhang, W.: Details preservation inspired blind quality metric of tone mapping methods. In: Proceedings of the IEEE International Symposium on Circuits and Systems, pp. 518-521, Jun 2014Google Scholar
  15. 15.
    Wang, Z., Bovik, A.C.: Mean squared error: love it or leave it? A new look at signal fidelity measures. IEEE Signal Process. Mag. 26(1), 98–117 (2009)CrossRefGoogle Scholar
  16. 16.
    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)CrossRefGoogle Scholar
  17. 17.
    Wang, Z., Simoncelli, E.P., Bovik, A.C.: Multi-scale structural similarity for image quality assessment. In: Proceedings of the IEEE Asilomar Conference on Signals, Systems and Computers, pp. 1398–1402, Nov 2003Google Scholar
  18. 18.
    Wang, Z., Li, Q.: Information content weighting for perceptual image quality assessment. IEEE Trans. Image Process. 20(5), 1185–1198 (2011)MathSciNetCrossRefGoogle Scholar
  19. 19.
    He, L., Gao, X., Lu, W., Li, X., Tao, D.: Image quality assessment based on S-CIELAB model. Signal Image Video Process. 7(3), 283–290 (2011)CrossRefGoogle Scholar
  20. 20.
    Gu, K., Zhai, G., Yang, X., Zhang, W.: A new psychovisual paradigm for image quality assessment: from differentiating distortion types to discriminating quality conditions. Signal Image Video Process. 7(3), 423–436 (2013)CrossRefGoogle Scholar
  21. 21.
    Dumic, E., Grgic, S., Grgic, M.: IQM2: new image quality measure based on steerable pyramid wavelet transform and structural similarity index. Signal Image Video Process. 8(6), 1159–1168 (2014)CrossRefGoogle Scholar
  22. 22.
    Gu, K., Liu, M., Zhai, G., Yang, X., Zhang, W.: Quality assessment considering viewing distance and image resolution. IEEE Trans. Broadcast. 61(3), 520–531Google Scholar
  23. 23.
    Larson, E.C., Chandler, D.M.: Most apparent distortion: full-reference image quality assessment and the role of strategy. J. Electron. Imaging 19(1), (2010)Google Scholar
  24. 24.
    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)MathSciNetCrossRefGoogle Scholar
  25. 25.
    Narwaria, M., Lin, W., Enis Cetin, A.: Scalable image quality assessment with 2D mel-cepstrum and machine learning approach. Pattern Recognit. 45(1), 299–313 (2012)CrossRefGoogle Scholar
  26. 26.
    Liu, A., Lin, W., Narwaria, M.: Image quality assessment based on gradient similarity. IEEE Trans. Image Process. 21(4), 1500–1512 (2012)MathSciNetCrossRefGoogle Scholar
  27. 27.
    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
  28. 28.
    Gu, K., Zhai, G., Yang, X., Zhang, W.: An efficient color image quality metric with local-tuned-global model. In: Proceedings of the IEEE International Conference on Image Processing, pp. 506–510. IEEE, Paris, Oct 2014Google Scholar
  29. 29.
    Xue, W., Mou, X., Zhang, L., Feng, X.: Perceptual fidelity aware mean squared error. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 705–712, Dec 2013Google Scholar
  30. 30.
    Simoncelli, E.P., Olshausen, B.A.: Natural image statistics and neural representation. Annu. Rev. Neurosci. 24(1), 1193–1216 (2001)CrossRefGoogle Scholar
  31. 31.
    Morrone, M.C., Ross, J., Burr, D.C., Owens, R.: Mach bands are phase dependent. Nature 324, 250–253 (1986)CrossRefGoogle Scholar
  32. 32.
    Friston, K.: The free-energy principle: a unified brain theory? Nat. Rev. Neurosci. 11, 127–138 (2010)CrossRefGoogle Scholar
  33. 33.
    Wang, S., Zhang, X., Ma, S., Gao, W.: Reduced reference image quality assessment using entropy of primitives. In: Picture Coding Symposium, pp. 193–196, 2013Google Scholar
  34. 34.
    Suhre, A., Kose, K., Enis Cetin, A., Gurcan, M.N.: Content-adaptive color transform for image compression. Opt. Eng. 50(5), (2011)Google Scholar
  35. 35.
    Soundararajan, R., Bovik, A.C.: Survey of information theory in visual quality assessment. Signal Image Video Process. 7(3), 391–401 (2013)CrossRefGoogle Scholar
  36. 36.
    VQEG: final report from the video quality experts group on the validation of objective models of video quality assessment. Mar 2000.
  37. 37.
    Zhai, G., Wu, X., Yang, X., Lin, W., Zhang, W.: A psychovisual quality metric in free-energy principle. IEEE Trans. Image Process. 21(1), 41–52 (2012)MathSciNetCrossRefGoogle Scholar
  38. 38.
    Gu, K., Zhai, G., Yang, X., Zhang, W.: Using free energy principle for blind image quality assessment. IEEE Trans. Multimed. 17(1), 50–63Google Scholar

Copyright information

© Springer-Verlag London 2015

Authors and Affiliations

  • Ke Gu
    • 1
    • 2
    • 3
    Email author
  • Shiqi Wang
    • 1
  • Guangtao Zhai
    • 2
    • 3
  • Siwei Ma
    • 4
  • Xiaokang Yang
    • 2
    • 3
  • Wenjun Zhang
    • 2
    • 3
  1. 1.Department of Electrical and Computer EngineeringUniversity of WaterlooWaterlooCanada
  2. 2.Institute of Image Communication and Information ProcessingShanghai Jiao Tong UniversityShanghaiChina
  3. 3.Shanghai Key Laboratory of Digital Media Processing and TransmissionsShanghaiChina
  4. 4.Institute of Digital MediaPeking UniversityBeijingChina

Personalised recommendations