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A no-reference image quality measure based on CPBD and noise

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DOI: 10.23977/jeis.2016.11003 | Downloads: 74 | Views: 6513

Author(s)

Yufeng Gu 1, Kejian Yang 1

Affiliation(s)

1 Computer Science and Technology, Wuhan University of Technology, Wuhan, Hubei 430063, China

Corresponding Author

Yufeng Gu

ABSTRACT

In order to meet the needs of the objective evaluation of no-reference image, a no-reference image quality measure is presented. The measure is based on edge analysis and is suitable for images with noise. Taking properties of the Human Visual System(HVS) into account, we compute the probability of blur after getting the edge width and the local contrasts. And at last the image quality probability can be got considering cumulative probability of blur detection and the noise pollution degree. Experimental results show that the metric has a wide application, good anti-noise ability, simple calculation, as well as in high consistence with the subjective evaluation results.

KEYWORDS

No-reference; Image Quality Assessment; CPBD; Noise pollution.

CITE THIS PAPER

Yufeng, G. and Kejian, Y. (2016) A no-reference image quality measure based on CPBD and noise. Journal of Electronics and Information Science (2016) 1: 11-16.

REFERENCES

[1] Ong E P, Lin W, Lu Z, et al. A no-reference quality metric for measuring image blur[C]// Signal Processing and Its Applications, 2003. Proceedings. Seventh International Symposium on. IEEE, 2003:469-472 vol.1.
[2] Ong E P, Lin W, Lu Z, et al. No-reference JPEG-2000 image quality metric[C]// Multimedia and Expo, 2003. ICME '03. Proceedings. 2003 International Conference on. IEEE, 2003:I-545-8 vol.1.    
[3] Yan Q, Xu Y, Yang X. No-reference image blur assessment based on gradient profile sharpness[C]// Broadband Multimedia Systems and Broadcasting (BMSB), 2013 IEEE International Symposium on. IEEE, 2013:1-4.
[4] Wang Y, Du H, Xu J, et al. A no-reference perceptual blur metric based on complex edge analysis[C]// Network Infrastructure and Digital Content (IC-NIDC), 2012 3rd IEEE International Conference on. IEEE, 2012:487-491.
[5] Ferzli R, Karam L J. Human Visual System Based No-Reference Objective Image Sharpness Metric[C]// IEEE International Conference on Image Processing. 2006:2949-2952. 
[6] Ferzli R, Karam L J. A no-reference objective image sharpness metric Based on just-noticeable blur and probability summation[C]// IEEE International Conference on Image Processing. 2007(3): III-445−III-448 
[7] Immerkær J. Fast Noise Variance Estimation[J]. Computer Vision & Image Understanding, 1996, 64(2):300-302.
[8] Laligant O, Truchetet F, Fauvet E. Noise estimation from digital step-model signal.[J]. IEEE Transactions on Image Processing A Publication of the IEEE Signal Processing Society, 2013, 22(12):5158-67.
[9] Narvekar N D, Karam L J. A no-reference perceptual image sharpness metric based on a cumulative probability of blur detection[C]// Quality of Multimedia Experience, 2009. QoMEx 2009. International Workshop on. 2009:87 - 91.
[10] Rudin L I, Osher S, Fatemi E. Nonlinear total variation based noise removal algorithms[J]. Physica D Nonlinear Phenomena, 1992, 60(1-4):259-268.
[11] Feichtenhofer C, Fassold H, Schallauer P. A Perceptual Image Sharpness Metric Based on Local Edge Gradient Analysis[J]. IEEE Signal Processing Letters, 2013, 20(4):379-382.
[12] H. R. Sheikh, Z. Wang, L. Cormack and A. C. Bovik, "LIVE Image Quality Assessment Database Release 2", http://live.ece.utexas.edu/research/quality.

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