Abstract
Image quality assessment (Anmin et al. in Image Proc, IEEE Trans 21(4):1500–1512, 2012 [1]) is very important for image processing. A good image evaluation algorithm is consistent with subjective evaluations and has low computational complexity. A lot of image quality assessment methods have been proposed in recent years. Structural Contrast Index (SCI) has been proved can effectively reflect the complexity of image texture and model the masking effect of human visual system (HVS), so SCI is used as an important feature. HVS is very sensitive to edge region, however, SCI can’t correctly model the edge region structure. So the gradient similarity was incorporated into our method. An image quality assessment scheme based on structural contrast index and gradient similarity was proposed in our paper. Extensive experiments conducted on TID2013 image database demonstrate the performance this scheme is slightly better than the state-of-art methods not only on prediction accuracy but computational complexity.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Anmin, L., Weisi, L., & Narwaria, M. (2012). Image Quality Assessment Based on Gradient Similarity. Image Processing, IEEE Transactions on. 10.1109/TIP.2011.2175935/21(4), 1500–1512.
Zhou, W., Bovik, A. C., Sheikh, H. R., & Simoncelli, E. P. (2004). Image quality assessment: from error visibility to structural similarity. Image Processing, IEEE Transactions on. 10.1109/TIP.2003.819861/13(4), 600–612.
Wang, Z., Simoncelli, E. P., & Bovik, A. C. (2003). Multiscale structural similarity for image quality assessment. Journal.10.1109/ACSSC.2003.1292216/2, 1398–1402 Vol. 1392.
Jieying, Z., & Nengchao, W. (2012). Image Quality Assessment by Visual Gradient Similarity. Image Processing, IEEE Transactions on. 10.1109/TIP.2011.2169971/21(3), 919–933.
Guangquan, C., JinCai, H., Cheng, Z., Zhong, L., & Lizhi, C. (2010). Perceptual image quality assessment using a geometric structural distortion model. Journal. 10.1109/ICIP.2010.5649265/325–328.
Wufeng, X., Lei, Z., Xuanqin, M., & Bovik, A. C. (2014). Gradient Magnitude Similarity Deviation: A Highly Efficient Perceptual Image Quality Index. Image Processing, IEEE Transactions on. 10.1109/TIP.2013.2293423/23(2), 684–695.
Lin, Z., Zhang, D., Xuanqin, M., & Zhang, D. (2011). FSIM: A Feature Similarity Index for Image Quality Assessment. Image Processing, IEEE Transactions on. 10.1109/TIP.2011.2109730/20(8), 2378–2386.
Zhang, L., Shen, Y., & Li, H. (2014). VSI: A Visual Saliency-Induced Index for Perceptual Image Quality Assessment. IEEE Transactions on Image Processing.10.1109/TIP.2014.2346028/23(10), 4270–4281.
Bae, S. H., & Kim, M. (2014). A Novel Generalized DCT-Based JND Profile Based on an Elaborate CM-JND Model for Variable Block-Sized Transforms in Monochrome Images. IEEE Transactions on Image Processing.10.1109/TIP.2014.2327808/23(8), 3227–3240.
Bae, S. H., & Kim, M. (2016). A Novel Image Quality Assessment With Globally and Locally Consilient Visual Quality Perception. IEEE Transactions on Image Processing.10.1109/TIP.2016.2545863/25(5), 2392–2406.
Ponomarenko, N., Ieremeiev, O., Lukin, V., Egiazarian, K., Jin, L., Astola, J., … Kuo, C. C. J. (2013). Color image database TID2013: Peculiarities and preliminary results. Journal. 106–111.
Acknowledgements
This study is funded by Shaanxi Provincial Key Laboratory of project-Printing image quality assessment based on human visual features (13JS082).
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
Liu, L., Zheng, Y., Wang, W. (2017). Image Quality Assessment Scheme Based on Structural Contrast Index and Gradient Similarity. In: Zhao, P., Ouyang, Y., Xu, M., Yang, L., Ouyang, Y. (eds) Advanced Graphic Communications and Media Technologies . PPMT 2016. Lecture Notes in Electrical Engineering, vol 417. Springer, Singapore. https://doi.org/10.1007/978-981-10-3530-2_41
Download citation
DOI: https://doi.org/10.1007/978-981-10-3530-2_41
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-10-3529-6
Online ISBN: 978-981-10-3530-2
eBook Packages: EngineeringEngineering (R0)