Please wait a minute...
J4  2014, Vol. 48 Issue (2): 312-320    DOI: 10.3785/j.issn.1008-973X.2014.02.019
Sharpness metric based on histogram of strong edge width
ZHANG Tian-yu, FENG Hua-jun, XU Zhi-hai, LI Qi, CHEN Yue-ting
State Key Laboratory of Modern Optical Instrumentation, Zhejiang University, Hangzhou, 310027, China
Download:   PDF(3084KB) HTML
Export: BibTeX | EndNote (RIS)      


This paper presented a novel no-reference sharpness/blurriness metric due to its importance in image, video applications. Based on the principle that image blurring caused edge to spread, this algorithm used a self-adapting gradient operator to compute a gradient image, which contained strong edges. The width of each strong edge was calculated and its histogram was generated. Based on the information of histogram, a distance factor was proposed. The factor was introduced into the model, which could gain the average width of strong edges by the weighted mean method, and the sharpness metric could be obtained. Comparing with the state-of-the-art edge width based sharpness evaluation, this algorithm works more efficiently. According to experimental results, the proposed method shows its good performance in content independence and high correlation with subjective assessment.

Published: 01 February 2014
CLC:  TP 391  
Cite this article:

ZHANG Tian-yu, FENG Hua-jun, XU Zhi-hai, LI Qi, CHEN Yue-ting. Sharpness metric based on histogram of strong edge width. J4, 2014, 48(2): 312-320.

URL:     OR



[1] LIU Han-Tao, WANG Jun-Le, REDI J, et al. An efficient no-reference metric for perceived blur [C]∥ Proceedings of the 3rd European Workshop on Visual Information Processing. France: IEEE, 2011: 174-179.
[2] SUN Yu, DUTHALER S, NELSON B J. Autofocusing in computer microscopy: selecting the optimal focus algorithm [J]. Microscopy Research and Technique, 2004, 65: 139-149.
[3] LIU Han-Tao, HEYNDERICKX I. Issues in the design of a no-reference metric for perceived blur [C]∥ Proceedings of SPIE-IS&T Electronic Imaging 2011 and Image Quality and System Performance VIII. United States: SPIE, 2011, 7867: 0C10C8
[4] MARICHAL X, MA Wei-Ying, ZHANG Hong-Jiang. Blur determination in the compressed domain using DCT information [C]∥ Proceedings of the 1999 International Conference on Image Processing. Japan: IEEE, 1999: 386-390.
[5] CAVIEDES J, GURBUZ S. No-reference sharpness metric based on local edge kurtosis [C]∥ Proceedings of the 2002 International Conference on Image Processing. United States: IEEE, 2002: 53-56.
[6] CAVIEDES J, OBERTI F. A new sharpness metric based on local kurtosis, edge and energy information [J]. Signal Processing: Image Communication, 2004, 19: 147-161.
[7] HASSEN R, ZHOU Wang, SALAMA M. No-reference image sharpness assessment based on local phase coherence measurement [C]∥ Proceedings of the 2010 IEEE International Conference on Acoustics Speech and Signal Processing. United States: IEEE, 2010: 2434-2437.
[8] MARZILIANO P, DUFAUX F, WINKLER S, et al. Perceptual blur and ringing metrics: application to JPEG2000 [J]. Signal Processing: Image Communication, 2004, 19: 163-172.
[9] WANG Xin, TIAN Bao-Feng, LIANG Chao, et al. Blind image quality assessment for measuring image blur [C]∥ Proceedings of the 2008 Congress on Image and Signal Processing. China: IEEE, 2008: 467-470.
[10] FERZIL R, KARAM L J. A no-reference objective image sharpness metric based on the notion of just noticeable blur (JNB) [J]. IEEE Transactions on Image Processing, 2009, 18(4): 717-728.
[11] NARVEKAR N D, KARAM L J. An improved no-reference sharpness metric based on the probability of blur detection [C]∥ Proceedings of the fifth International Workshop on Video Processing and Quality Metrics for Consumer Electronics. United States: [s. n.], 2010.
[12] WILLIAM K P. Digital imaging processing: PIKS scientific inside [M]. 4th ed. United States: John Wiley & Sons, Inc., 2007.
[13] SHEIKH H R, ZHOU Wang, CORMACK L, et al. LIVE image quality assessment database release 2 [EB/OL]. [2006]. http:∥

[1] ZHAO Jian-jun, WANG Yi, YANG Li-bin. Threat assessment method based on time series forecast[J]. J4, 2014, 48(3): 398-403.
[2] CUI Guang-mang, ZHAO Ju-feng,FENG Hua-jun, XU Zhi-hai,LI Qi, CHEN Yue-ting. Construction of fast simulation model for degraded image by inhomogeneous medium[J]. J4, 2014, 48(2): 303-311.
[3] LIU Zhong, CHEN Wei-hai, WU Xing-ming, ZOU Yu-hua, WANG Jian-hua. Salient region detection based on stereo vision[J]. J4, 2014, 48(2): 354-359.
[4] WANG Xiang-bing,TONG Shui-guang,ZHONG Wei,ZHANG Jian. Study on  scheme design technique for hydraulic excavator's structure performance based on extension reuse[J]. J4, 2013, 47(11): 1992-2002.
[5] WANG Jin, LU Guo-dong, ZHANG Yun-long. Quantification-I theory based IGA and its application[J]. J4, 2013, 47(10): 1697-1704.
[6] LIU Yu, WANG Guo-jin. Designing  developable surface pencil through  given curve as its common asymptotic curve[J]. J4, 2013, 47(7): 1246-1252.
[7] HU Gen-sheng, BAO Wen-xia, LIANG Dong, ZHANG Wei. Fusion of panchromatic image and multi-spectral image based on
SVR and Bayesian method 
[J]. J4, 2013, 47(7): 1258-1266.
[8] WU Jin-liang, HUANG Hai-bin, LIU Li-gang. Texture details preserving seamless image composition[J]. J4, 2013, 47(6): 951-956.
[9] CHEN Xiao-hong,WANG Wei-dong. A HDTV video de-noising algorithm based on spatial-temporal filtering[J]. J4, 2013, 47(5): 853-859.
[10] ZHU Fan , LI Yue, JIANG Kai, YE Shu-ming, ZHENG Xiao-xiang. Decoding of rat’s primary motor cortex by partial least square[J]. J4, 2013, 47(5): 901-905.
[11] WU Ning, CHEN Qiu-xiao, ZHOU Ling, WAN Li. Multi-level method of optimizing vector graphs converted from remote sensing images[J]. J4, 2013, 47(4): 581-587.
[12] JI Yu, SHEN Ji-zhong, SHI Jin-he. Automatic ocular artifact removal based on blind source separation[J]. J4, 2013, 47(3): 415-421.
[13] WANG Xiang, DING Yong. Full reference image quality assessment based on Gabor filter[J]. J4, 2013, 47(3): 422-430.
[14] TONG Shui-guang, WANG Xiang-bing, ZHONG Wei, ZHANG Jian. Dynamic optimization design for rigid landing leg of crane
based on BP-HGA
[J]. J4, 2013, 47(1): 122-130.
[15] LIU Fang, SUN Yun, YANG Geng, LIN Hai. Visualization of social network based on particle swarm optimization[J]. J4, 2013, 47(1): 37-43.