CUI Ziguan, GAN Zongliang, TANG Guijin, LIU Feng, ZHU Xiuchang. Image Signature Based Mean Square Error for Image Quality Assessment[J]. Chinese Journal of Electronics, 2015, 24(4): 755-760. doi: 10.1049/cje.2015.10.015
Citation: CUI Ziguan, GAN Zongliang, TANG Guijin, LIU Feng, ZHU Xiuchang. Image Signature Based Mean Square Error for Image Quality Assessment[J]. Chinese Journal of Electronics, 2015, 24(4): 755-760. doi: 10.1049/cje.2015.10.015

Image Signature Based Mean Square Error for Image Quality Assessment

doi: 10.1049/cje.2015.10.015
Funds:  This work is supported by the National Natural Science Foundation of China (NSFC) (No.61471201, No.61471203, No.61172118), Natural Science Foundation of Jiangsu Province (No.BK20130867), Jiangsu Province Higher Education Institutions Natural Science Research Key Grant Project (No.13KJA510004), Natural Science Foundation of NJUPT (No.NY212015, No.NY214031), and 1311 Talent Program of NJUPT.
  • Received Date: 2014-10-30
  • Rev Recd Date: 2015-04-07
  • Publish Date: 2015-10-10
  • Motivated by the importance of Human visual system (HVS) in image processing, we propose a novel Image signature based mean square error (ISMSE) metric for full reference Image quality assessment (IQA). Efficient image signature based describer is used to predict visual saliency map of the reference image. The saliency map is incorporated into luminance difference between the reference and distorted images to obtain image quality score. The effect of luminance difference on visual quality with larger saliency value which is usually corresponding to foreground objects is highlighted. Experimental results on LIVE database release 2 show that by integrating the effects of image signature based saliency on luminance difference, the proposed ISMSE metric outperforms several state-of-the-art HVS-based IQA metrics but with lower complexity.
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