Abstract
High dynamic range image can provide wider dynamic range and more image details, it is needed a tone-mapping operator in order to be showed on an ordinary display, how to evaluate the tone-mapped image becomes an important problem to be solved. The distortion of tone-mapped image is different from the traditional image distortion, so, this paper proposes an objective quality evaluation algorithm of tone-mapped image based on color space which considers the difference between the reference and test images, the structural fidelity, the color distortion and the naturalness of the test image. Finally, the support vector machine is used as the pooling strategy to set up the quality assessment model. The experimental results show that the Pearson linear correlation coefficient of the proposed method is about 0.86, the Spearman rank correlation coefficient is about 0.84, which means that the proposed method is consistent with human visual perception.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Artusi, A., Mantiuk, R.K., Richter, T., et al.: JPEG XT: a compression standard for HDR and WCG images. IEEE Signal Process. Mag. 33(2), 118–124 (2016)
Ma, K., Yeganeh, H., Zeng, K., et al.: High dynamic range image compression by optimizing tone-mapped image quality index. IEEE Trans. Image Process. 24(10), 3086–3097 (2015)
Eilertsen, G., Mantiuk, R.K., Unger, J.: Real-time noise-aware tone mapping. ACM Trans. Graph. 34(6), 198:1–198:15 (2015)
Xue, W., Zhang, L., Mou, X., et al.: Gradient magnitude similarity deviation: a highly efficient perceptual image quality index. IEEE Trans. Image Process. 23(2), 684–695 (2014)
Zhang, L., Zhang, L., Mou, X., et al.: FSIM: a feature similarity index for image quality assessment. IEEE Trans. Image Process. 20(8), 2378–2386 (2011)
Liu, A.M., Lin, W.S., Narwaria, M.: Image quality assessment based on gradient similarity. IEEE Trans. Image Process. 21(4), 1500–1512 (2012)
Wang, Z., Simoncelli, E.P., Bovik, A.C.: Multi-scale structural similarity for image quality assessment. Asilomar Conf. Signals Syst. Comput. 2, 1398–1402 (2003)
Narwaria, M., Silva, M.P.D., Callet, P.L.: HDR-VQM: an objective quality measure for high dynamic range video. Sig. Process. Image Commun. 35, 46–60 (2015)
Mantiuk, R., Kim, K.J., Rempel, A.G., et al.: HDR-VDP-2: a calibrated visual metric for visibility and quality predictions in all luminance conditions. ACM Trans. Graph. 30(4), 76–79 (2011)
Yeganeh, H., Wang, Z.: Objective quality assessment of tone-mapped images. IEEE Trans. Image Process. 22(2), 657–667 (2013)
Nafchi, H.Z., Shahkolaei, A., Moghaddam, R.F., et al.: FSITM: a feature similarity index for tone-mapped images. IEEE Signal Process. Lett. 22(8), 1026–1029 (2015)
Xie, J.: Principles and Applications of Vision Bionics. Science Press, Beijing (2013)
Cadik, M., Slavik, P.: The naturalness of reproduced high dynamic range images. Int. Conf. Inf. Vis. 24(11), 920–925 (2005)
Appina, B., Khan, S., Channappayya, S.: No-reference stereoscopic image quality assessment using natural scene statistics. Sig. Process. Image Commun. 43, 1–14 (2016)
Besrour, A., Abdelkefi, F., Siala, M., et al.: Luminance and contrast ideal balancing based tone mapping algorithm. In: Proceedings of SPIE, vol. 9598 (2015)
Acknowledgement
This work was supported by Natural Science Foundation of China (61671258) and the Natural Science Foundation of Zhejiang Province, China (LY15F010005).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Song, H., Jiang, G., Shao, H., Yu, M. (2017). New Tone-Mapped Image Quality Assessment Method Based on Color Space. In: Yuan, H., Geng, J., Bian, F. (eds) Geo-Spatial Knowledge and Intelligence. GRMSE 2016. Communications in Computer and Information Science, vol 698. Springer, Singapore. https://doi.org/10.1007/978-981-10-3966-9_42
Download citation
DOI: https://doi.org/10.1007/978-981-10-3966-9_42
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-10-3965-2
Online ISBN: 978-981-10-3966-9
eBook Packages: Computer ScienceComputer Science (R0)