nach oben

Wireless Personal Communications

Erschienen in:

21.02.2018

Quality Assessment of Blur Images Via Saliency and Multiscale Features

verfasst von: Zhongting Sun, Gang Hua, Yonggang Xu

Erschienen in: Wireless Personal Communications | Ausgabe 1/2018

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config

KI-gestützte Suche

Aus

Abstract

Blur is the most common distortion in images, so it is significant to evaluate the quality of blur images. Considering the view saliency and multiscale characteristics of human vision, this paper proposes a fast and efficient blur quality assessment method. Firstly, spatial sharpness and saliency maps are extracted, and the saliency map is used as the weight of the sharpness map. At the same time, images are downsampled to multiscales and the texture naturalness of the image on each scale is measured. Then all features are used to train the support vector regression model. Finally, the model is employed to evaluate the quality of blur images. Experimental results conducted on four image databases demonstrate that comparing with existing blur quality assessment methods and general quality metrics, the presented method has obvious superiority on performance and computation speed.

Vorheriger Artikel Research on the Influence of District Opening of Urban Road Base on Multi-decision and Network Vulnerability Models

Nächster Artikel Video Copy Detection Based on Deep CNN Features and Graph-Based Sequence Matching

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 340 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Marziliano, P., Dufaux, F., Winkler, S., & Ebrahimi, T. (2004). Perceptual blur and ringing metrics: Application to JPEG2000. Signal Processing: Image Communication, 19(2), 163–172.

Narvekar, N. D., & Karam, L. J. (2011). A no-reference image blur metric based on the cumulative probability of blur detection (CPBD). IEEE Transactions on Image Processing, 20(9), 2678–2683.MathSciNetCrossRef

Vu, C. T., Phan, T. D., & Chandler, D. M. (2012). S3: A spectral and spatial measure of local perceived sharpness in natural images. IEEE Transactions on Image Processing, 21(3), 934–945.MathSciNetCrossRef

Vu, P. V., & Chandler, D. M. (2012). A fast wavelet-based algorithm for global and local Image sharpness estimation. IEEE Signal Processing Letters, 19(7), 423–426.CrossRef

Sang, Q. B., Qi, H. X., Wu, X. J., Li, C. F., & Bovik, A. C. (2014). No-reference image blur index based on singular value curve. Journal of Visual Communication and Image Representation, 25(7), 1625–1630.CrossRef

Gu, K., Zhai, G. T., Lin, W. S., Yang, X. K., & Zhang, W. J. (2015). No-reference image sharpness assessment in autoregressive parameter space. IEEE Transactions on Image Processing, 24(10), 3218–3231.MathSciNetCrossRef

Li, Z., Cheng, X., Gustafson, W. I., Jr., et al. (2016). Spectral characteristics of background error covariance and multiscale data assimilation. International Journal for Numerical Methods in Fluids, 82(12), 1035–1048.MathSciNetCrossRef

Kotera, J., Zitová, B., Šroubek, F. (2015). PSF accuracy measure for evaluation of blur estimation algorithms. In: IEEE international conference on image processing (pp. 2080–2084).

Li, L. D., Yan, Y., Fang, Y. M., Wang, S. Q., Tang, L., & Qian, J. S. (2016). Perceptual quality evaluation for image defocus deblurring. Signal Processing: Image Communication. https://doi.org/10.1016/j.image.2016.09.005.CrossRef

10.

Li, L., Yan, Y., Fang, Y., et al. (2016). Perceptual quality evaluation for image defocus deblurring. Signal Processing: Image Communication, 48, 81–91.

11.

Chang, C. C., & Lin, C. J. (2011). LIBSVM: A library for support vector machines. ACM Transactions on Intelligent Systems and Technology, 2(3), 1–27.CrossRef

12.

Sheikh, H. R., Sabir, M. F., & Bovik, A. C. (2006). A statistical evaluation of recent full reference image quality assessment algorithms. IEEE Transactions on Image Processing, 15(11), 3440–3451.CrossRef

13.

Larson, E. C., & Chandler, D. M. (2010). Most apparent distortion: Full-reference image quality assessment and the role of strategy. Journal of Electronic Imaging, 19(1), 001006:1–001006:21.

14.

Ponomarenko, N., Lukin, V., Zelensky, A., Egiazarian, K., Carli, M., & Battisti, F. (2009). TID2008-a database for evaluation of full-reference visual quality assessment metrics. Advances of Modern Radioelectronics, 10(4), 30–45.

15.

Zhang, J., Wang, Y., & Zhang, Y. (2016). Chroma’s revision capacity for traditional algorithms in evaluating enhanced image. Video Engineering, 40(9), 100–102.

16.

Wang, Z., & Li, Q. (2011). Information content weighting for perceptual image quality assessment. IEEE Transactions on Image Processing, 20(5), 1185–1198.MathSciNetCrossRef

17.

Moorthy, A. K., & Bovik, A. C. (2010). A two-step framework for constructing blind image quality indices. IEEE Signal Processing Letters, 17(5), 513–516.CrossRef

18.

Saad, M. A., & Bovik, A. C. (2012). Blind image quality assessment: A natural scene statistics approach in the DCT domain. IEEE Transactions on Image Processing, 21(8), 3339–3352.MathSciNetCrossRef

19.

Mittal, A., Moorthy, A. K., & Bovik, A. C. (2012). No-reference image quality assessment in the spatial domain. IEEE Transactions on Image Processing, 21(12), 4695–4708.MathSciNetCrossRef

20.

Moorthy, A. K., & Bovik, A. C. (2011). Blind image quality assessment: From natural scene statistics to perceptual quality. IEEE Transactions on Image Processing, 20(12), 3350–3364.MathSciNetCrossRef

Titel: Quality Assessment of Blur Images Via Saliency and Multiscale Features
verfasst von: Zhongting Sun
Gang Hua
Yonggang Xu
Publikationsdatum: 21.02.2018
Verlag: Springer US
Erschienen in: Wireless Personal Communications / Ausgabe 1/2018
Print ISSN: 0929-6212
Elektronische ISSN: 1572-834X
DOI: https://doi.org/10.1007/s11277-018-5449-3

Springer Professional

Abstract

Bitte loggen Sie sich ein, um Zugang zu Ihrer Lizenz zu erhalten.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Technik"

Springer Professional "Wirtschaft+Technik"

Springer Professional "Wirtschaft"

Weitere Artikel der Ausgabe 1/2018

The Experiment Research of Continuous-Wave Terahertz Imaging System

Trust Value Evolutionary Simulation Based a Whole-process and Multi-round Opinion Propagative Model

The Effect of Satisfaction on Loyalty in Consumption and Service Industry Based on Meta-analysis and It’s Algorithm

Study on Replica Strategy Based on Access Pattern Mining in Smart City Cloud Storage System

Research and Evaluation on NC Programming and Simulation Based on Keller SYMplus CNC

Perturbation Sensitivity Analysis and Dynamic Topology Optimization for Heat Conduction Structure