Abstract
image quality assessment plays an important role in relevant fields of image processing. The traditional image quality metric, such as PSNR, cannot reflect the visual perception to the image effectively. For this purpose, based on the fuzzy Sugeno integral a novel image quality assessment measure, called content-based metric (CBM), is proposed in this paper. It fuses the amount and local information into the similarity of the image structural information and gives a comprehensive evaluation for the quality of the specified image. The experimental results illustrate that the proposed metric has a good correlation with the human subjective perception, and can reflect the image quality effectively.
This work was supported by the National Natural Science Foundation of China (No.60202004) and the Key project of Chinese Ministry of Education (No.104173).
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References
Kusuma, T.M., Zepernick, H.J.: A reduced-reference perceptual quality metric for in-service image quality assessment. In: Proc. Joint First Workshop on Mobile Future and IEEE Symposium on Trends in Communications, Bratislava, Slovakia, pp. 71–74. IEEE Press, Los Alamitos (2003)
Li, B., Meyer, W., Klassen Victor, R.: Comparison of two image quality models. SPIE Human Vision and Electronic Imaging III 2(3299), 98–109 (1998)
VQEG: Final report from the video quality experts group on the validation of objective models of video quality assessment 3 (2000), http://www.vqeg.org/
Wang, Z., Bovik, A.C.: A universal image quality index. IEEE Signal Processing Letters, 3(9), 81–84 (2002)
Wang, Z., Bovik, A.C., Lu, L.: Why is image quality assessment so difficult. In: Proc. IEEE Int. Conf. on Acoustics, Speech, and Signal Processing, Orlando, Florida, pp. 3313–3316. IEEE Press, Los Alamitos (2002)
Wang, Z., Bovik, A.C., Sheikh Hamid, R., Simoncelli Eero, P.: Image quality assessment: from error visibility to structural similarity. IEEE Trans. on Image Processing 4(13), 600–612 (2004)
Miyahara, M., Kotani, K., Algazi, V.R.: Objective picture quality scale (PQS) for image coding. IEEE Trans. on Commun. 9(46), 1215–1226 (1998)
Li, J., Chen, G., Chi, Z., Lu, C.: Image coding quality assessment using fuzzy integrals with a three-component image model. IEEE Trans. on Fuzzy Systems 1(12), 99–106 (2004)
Sugeno, M.: Theory of fuzzy integrals and its application. PhD dissertation, Tokyo institute of technology, Japan (1974)
Klir, G.J., Wang, Z.: Fuzzy Measure Theory. Kluwer, Norwell (2001)
Tahani, H., Keller, J.M.: Information fusion in computer vision using the fuzzy integral. IEEE Trans. on Syst., Man, Cybern. 3(20), 733–741 (1990)
Wang, Z.: the SSIM index for image quality assessment, http://www.cns.nyu.edu/~lcv/ssim/
Sheikh, H.R., Wang, Z., Bovik, A.C., Cormack, L.K.: Image and video quality assessment research at LIVE, http://live.ece.utexas.edu/research/quality/
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Gao, X., Wang, T., Li, J. (2005). A Content-Based Image Quality Metric. In: Ślęzak, D., Yao, J., Peters, J.F., Ziarko, W., Hu, X. (eds) Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing. RSFDGrC 2005. Lecture Notes in Computer Science(), vol 3642. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11548706_25
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DOI: https://doi.org/10.1007/11548706_25
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-28660-8
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