Abstract
Recently, the algorithms of general purpose blind image quality assessment (BIQA) have been an important research area in the field of image processing, but the previous approaches usually depend on human scores image for training and using the regression methods to predict the image quality. In this paper, we first apply the full-reference image quality measure to obtain the image quality scores for training to let our algorithm independent of the judgment of human. Then, we abstract features using an NSS model of the image DCT coefficient which is indicative of perceptual quality, and subsequently, we import Pairwise approach of Learning to rank (machine-learned ranking) to predict the perceptual scores of image quality. Our algorithm is tested on LIVE II and CSIQ database and it is proved to perform highly correlate with human judgment of image quality, and better than the popular SSIM index and competitive with the state-of-the-art BIQA algorithms.
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
Wang, Z., Bovik, A.C., Sheikh, H.R., et al.: Image quality assessment: from error visibility to structural similarity. Image Process. IEEE Trans. 13(4), 600–612 (2004)
Saad, M.A., Bovik, A.C., Charrier, C.: Blind image quality assessment: A natural scene statistics approach in the DCT domain. Image Process. IEEE Trans. 21(8), 3339–3352 (2012)
Boinovi, N., Konrad, J.: Motion analysis in 3D DCT domain and its application to video coding. Signal Process. Image Commun. 20(6), 510–528 (2005)
Sharifi, K., Leon-Garcia, A.: Estimation of shape parameter for generalized Gaussian distributions in subband decompositions of video. Circuits Syst. Video Technol. IEEE Trans. 5(1), 52–56 (1995)
Zhang, L., Zhang, L., Mou, X., et al.: FSIM: a feature similarity index for image quality assessment. Image Process. IEEE Trans. 20(8), 2378–2386 (2011)
Xue, W., Zhang, L., Mou, X.: Learning without human scores for blind image quality assessment. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 995–1002 (2013)
Ye, P., Kumar, J., Kang, L., et al.: Unsupervised feature learning framework for no-reference image quality assessment. In: 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1098–1105. IEEE (2012)
Hang, L.I.: A short introduction to learning to rank. IEICE Trans. Inf. Syst. 94(10), 1854–1862 (2011)
Cohen, E., Yitzhaky, Y.: No-reference assessment of blur and noise impacts on image quality. Signal Image Video Process. 4(3), 289–302 (2010)
Hou, W., Gao, X., Tao, D., et al.: Blind image quality assessment via deep learning. Neural Netw. Learn. Syst. IEEE Trans. 26(6), 1275–1286 (2015)
Acknowledgment
The work is supported by National High-Tech R&D Program (863 Program) under Grant 2015AA016402 and Shanghai Natural Science Foundation under Grant 14Z111050022.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing AG
About this paper
Cite this paper
Jiang, J., Zhou, Y., He, L. (2016). A New Blind Image Quality Assessment Based on Pairwise. In: Hirose, A., Ozawa, S., Doya, K., Ikeda, K., Lee, M., Liu, D. (eds) Neural Information Processing. ICONIP 2016. Lecture Notes in Computer Science(), vol 9950. Springer, Cham. https://doi.org/10.1007/978-3-319-46681-1_72
Download citation
DOI: https://doi.org/10.1007/978-3-319-46681-1_72
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-46680-4
Online ISBN: 978-3-319-46681-1
eBook Packages: Computer ScienceComputer Science (R0)