Abstract
Most of the existing assessment methods for color image quality consider little about the image content, which plays an essential role in indicating the distortion level with the color and structure information it contains. By incorporating the color image content with human color perception, we present a novel assessment metric for color distortions. The proposed method standardizes input images with a color perception transformation, and then the transformed images are divided into lightness part and chroma part. For each part, a region separation strategy based on image content is implemented. By calculating and pooling the similarity of each region using fuzzy integral, the final index is achieved. Experimental results on color-related distortions of TID2013 database show the superiority of this new approach and comparative experiments reveal the rationality and the validity of our method.
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Eskicioglu, A.M., Fisher, P.S.: Image quality measures and their performance. IEEE Trans. Image Commun. 43(12), 2959–2965 (1995)
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)
Omari, M., Abdelouahad, A.A., Hassouni, M.E., Cherifi, H.: Color image quality assessment measure using multivariate generalized Gaussian distribution. In: International Conference on Signal-Image Technology and Internet-Based Systems, Japan, pp. 195–200 (2013)
Kolaman, A., Yadid-Pecht, O.: Quaternion structural similarity: a new quality index for color images. IEEE Trans. Image Process. 21(4), 1526–1536 (2012)
Wang, Y., Zhu, M.: Color image quality assessment based on quaternion representation for the local variance distribution of RGB channels. In: 2nd International Congress on Image and Signal Processing, Tianjin, China, pp. 1–6 (2009)
Redi, J.A., Gastaldo, P., Heynderickx, I., Zunino, R.: Color distribution information for the reduced-reference assessment of perceived image quality. IEEE Trans. Circ. Syst. Video Technol. 20(12), 1757–1769 (2010)
Zhang, L., Zhang, D., Mou, X.: FSIM: a feature similarity index for image quality assessment. IEEE Trans. Image Process. 20(8), 2378–2386 (2011)
Xie, Z.X., Wang, Z.F.: Color image quality assessment based on image quality parameters perceived by human vision system. In: International Conference on Multimedia Technology, (ICMT), Ningbo, China, pp. 1–4 (2010)
He, L., Gao, X., Lu, W., Li, X., Tao, D.: Image quality assessment based on S-CIELAB model. Sig. Image Video Process. 5(3), 283–290 (2011)
Lissner, I., Preiss, J., Urban, P., Lichtenauer, M.S.: Image-difference prediction: from grayscale to color. IEEE Trans. Image Process. 22(2), 435–446 (2013)
Preiss, J., Fernandes, F., Urban, P.: Color-image quality assessment: from prediction to optimization. IEEE Trans. Image Process. 23(3), 1366–1378 (2014)
Zhang, X., Silverstein, D.A, Farrell, J.E., Wandell, B.A.: Color image quality metric S-CIELAB and its application on halftone texture visibility. In: Proceedings of Compcon 1997, pp. 44–48. IEEE (1997)
Lissner, I., Urban, P.: Toward a unified color space for perception-based image processing. IEEE Trans. Image Process. 21(3), 1153–1168 (2012)
Wang, T., Gao, X., Zhang, D.: An objective content-based image quality assessment metric. J. Image Graph. 12(6), 1002–1007 (2007)
Tahani, H., Keller, J.M.: Information fusion in computer vision using the fuzzy integral. IEEE Trans. Syst. Man Cybern. 20(3), 733–741 (1990)
Ponomarenko, N., Ieremeiev, O., Lukin, V., Egiazarian, K., Jin, L., Astola, J., Kuo, C.-C. J.: Color image database TID2013: peculiarities and preliminary results. In: Proceedings of 4th European Workshop on Visual Information Processing EUVIP 2013, Paris, France, pp. 106–111 (2013)
Acknowledgments
This research was supported partially by the National Natural Science Foundation of China (No. 61125204, No. 61372130, No. 61432014), the Fundamental Research Funds for the Central Universities (No. BDY081426, No. JB140214), the Program for New Scientific and Technological Star of Shaanxi Province (No. 2014KJXX-47), and the Project Funded by China Postdoctoral Science Foundation (No. 2014M562378).
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Yang, S., Lu, W., He, L., Gao, X. (2015). A Novel Image Quality Assessment for Color Distortions. In: He, X., et al. Intelligence Science and Big Data Engineering. Image and Video Data Engineering. IScIDE 2015. Lecture Notes in Computer Science(), vol 9242. Springer, Cham. https://doi.org/10.1007/978-3-319-23989-7_49
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DOI: https://doi.org/10.1007/978-3-319-23989-7_49
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