Abstract
In this paper, a simple, yet effective no-reference image sharpness index for both local and global image sharpness assessment is calculated as the maximized local between-class variation, which is generated by the introduced Fisher discriminant criterion. On the other hand, the idea of dividing the local region into two classes, which we define as content part and background, is adopted to obtain improved Local Binary Pattern (LBP). Based on such LBP and its ability to describe local structure information, a reduced-reference image quality assessment model is realized by combining the shift of LBP histograms and image sharpness index for describing the spatial distribution and structural intensity respectively. Rigorous experiments with three large benchmark databases demonstrate the effectiveness of the two proposed models for both no-reference sharpness and reduced-reference image quality assessment.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Bong, D., Khoo, B.: An efficient and training-free blind image blur assessment in the spatial domain. IEICE Trans. Inf. Syst. 7, 1864–1871 (2014)
Wang, Y., Ren, S., Dong, F.: A transformation-domain image reconstruction method for open electrical impedance tomography based on conformal mapping. IEEE Sens. J. 19(5), 1873–1883 (2019)
Chow, C.W., Shi, R.J., Liu, Y.C., et al.: Non-flickering 100Â m RGB visible light communication transmission based on a CMOS image sensor. Opt. Express 26(6), 7079 (2018)
Xie, H., Lei, H., He, Y., et al.: Deeply supervised full convolution network for HEp-2 specimen image segmentation. Neurocomputing 351(25), 77–86 (2019)
Li, L., Zhou, Y., Wu, J., Qian, J., Chen, B.: Color-enriched gradient similarity for retouched image quality evaluation. IEICE Trans. Inf. Syst. 3, 773–776 (2016)
Wu, J., Lin, W., Shi, G.: Image quality assessment with degradation on spatial structure. IEEE Signal Proc. Lett. 21(4), 437–440 (2014)
Phong, V., Damon, M.C.: A fast wavelet-based algorithm for global and local image sharpness estimation. IEEE Signal Proc Lett. 19(7), 423–426 (2012)
Gao, Q., Wang, Q., Huang, Y., Gao, X., Hong, X., Zhang, H.: Dimensionality reduction by integrating sparse representation and fisher criterion and its application. IEEE Trans. Image Process. 24(12), 5684–5695 (2015)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Li, Y., Wang, J., Wang, G. (2021). Image Quality Assessment with Local Contrast Estimator. In: Sun, F., Liu, H., Fang, B. (eds) Cognitive Systems and Signal Processing. ICCSIP 2020. Communications in Computer and Information Science, vol 1397. Springer, Singapore. https://doi.org/10.1007/978-981-16-2336-3_43
Download citation
DOI: https://doi.org/10.1007/978-981-16-2336-3_43
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-16-2335-6
Online ISBN: 978-981-16-2336-3
eBook Packages: Computer ScienceComputer Science (R0)