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

Signal, Image and Video Processing

Volumn 10, Issue 5, 2016, Pages 803-810

  Gu, Ke ^a,b,c   Wang, Shiqi ^a   Zhai, Guangtao ^b,c   Ma, Siwei ^d   Yang, Xiaokang ^b,c   Zhang, Wenjun ^b,c  

^a UNIVERSITY OF WATERLOO   (Canada)

^b SHANGHAI JIAO TONG UNIVERSITY   (China)

^c Shanghai Key Laboratory of Digital Media Processing and Transmission   (China)

^d PEKING UNIVERSITY   (China)

Author keywords

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

Indexed keywords

ERRORS; IMAGE COMPRESSION; MEAN SQUARE ERROR;

GAUSSIAN CONVOLUTION; HUMAN VISUAL PERCEPTION; IMAGE CONTENT; IMAGE QUALITY ASSESSMENT (IQA); MEAN SQUARED ERROR; QUALITY ASSESSMENT; SUBJECTIVE IMAGE QUALITY; WEIGHTED MEAN SQUARED ERRORS;

IMAGE QUALITY;

EID: 84944707469     PISSN: 18631703     EISSN: 18631711     Source Type: Journal    
DOI: 10.1007/s11760-015-0818-9     Document Type: Article

References (38)

1. Sheikh, H.R., Wang, Z., Cormack, L., Bovik, A.C.: LIVE image quality assessment database release 2. http://live.ece.utexas.edu/research/quality
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)
3. Larson, E.C., Chandler, D.M.: Categorical image quality (CSIQ) database. http://vision.okstate.edu/csiq
4. Ninassi, A., Le Callet, P., Autrusseau, F.: Subjective quality assessment-IVC database. http://www2.irccyn.ec-nantes.fr/ivcdb
5. Horita, Y., Shibata, K., Kawayoke, Y., Sazzad, Z.M.P.: MICT image quality evaluation database. http://mict.eng.u-toyama.ac.jp/mict/index2.html
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 2013
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 2011
8. Rehman, A., Wang, Z.: Reduced-reference image quality assessment by structural similarity estimation. IEEE Trans. Image Process. 21(8), 3378–3389 (2012)
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)
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)
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 2013
12. Yeganeh, H., Wang, Z.: Objective quality assessment of tone-mapped images. IEEE Trans. Image Process. 22(2), 657–667 (2013)
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 2013
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 2014
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)
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)
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 2003
18. Wang, Z., Li, Q.: Information content weighting for perceptual image quality assessment. IEEE Trans. Image Process. 20(5), 1185–1198 (2011)
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)
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)
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)
22. Gu, K., Liu, M., Zhai, G., Yang, X., Zhang, W.: Quality assessment considering viewing distance and image resolution. IEEE Trans. Broadcast. 61(3), 520–531
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)
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)
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)
26. Liu, A., Lin, W., Narwaria, M.: Image quality assessment based on gradient similarity. IEEE Trans. Image Process. 21(4), 1500–1512 (2012)
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)
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 2014
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 2013
30. Simoncelli, E.P., Olshausen, B.A.: Natural image statistics and neural representation. Annu. Rev. Neurosci. 24(1), 1193–1216 (2001)
31. Morrone, M.C., Ross, J., Burr, D.C., Owens, R.: Mach bands are phase dependent. Nature 324, 250–253 (1986)
32. Friston, K.: The free-energy principle: a unified brain theory? Nat. Rev. Neurosci. 11, 127–138 (2010)
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, 2013
34. Suhre, A., Kose, K., Enis Cetin, A., Gurcan, M.N.: Content-adaptive color transform for image compression. Opt. Eng. 50(5), (2011)
35. Soundararajan, R., Bovik, A.C.: Survey of information theory in visual quality assessment. Signal Image Video Process. 7(3), 391–401 (2013)
36. VQEG: final report from the video quality experts group on the validation of objective models of video quality assessment. Mar 2000. http://www.vqeg.org/
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)
38. Gu, K., Zhai, G., Yang, X., Zhang, W.: Using free energy principle for blind image quality assessment. IEEE Trans. Multimed. 17(1), 50–63