计算机科学 ›› 2014, Vol. 41 ›› Issue (Z6): 224-226.

• 模式识别与图像处理 • 上一篇    下一篇

数字融合图像质量的视觉信息保真度客观评价方法

华东,余宏生   

  1. 温州职业技术学院电气电子工程系 温州325035;湖北理工学院数理学院 黄石435000
  • 出版日期:2018-11-14 发布日期:2018-11-14

Digital Fusion Image Quality Objective Assessment Method Based on Visual Information Fidelity

HUA Dong and YU Hong-sheng   

  • Online:2018-11-14 Published:2018-11-14

摘要: 在实时数字图像融合系统中需要对融合图像的质量进行客观评价。针对传统客观评价方法存在与人眼视觉系统(Human Visual System,HVS)主观评价结果不相符的缺陷,提出了一种基于视觉信息保真度(Visual Information Fidelity,VIF)的融合图像质量客观评价方法。在假设图像源符合GSM(Gaussian Scale Mixture,GSM)模型,并将图像融合处理作为图像信号失真通道,而且考虑人眼视觉噪声特性的前提下,建立了基于VIF的数字融合图像质量客观评价理论模型,提出了一种融合图像质量评价指标FVIF。实验结果表明:该方法能够对融合图像质量进行综合评价,与其它方法相比,FVIF客观评价方法的性能更好,其评价结果与主观评价结果基本一致。

关键词: 融合图像质量评价,视觉信息保真度,高斯尺度混合模型,人眼视觉系统 中图法分类号TP391.4文献标识码A

Abstract: Objective quality assessment of fusion image is of fundamental necessary in real-time digital image fusion system.A fusion image quality objective assessment method based on visual information fidelity (VIF) was proposed considering the assessment results of traditional objective assessment methods are disagree with the human subjective assessment results.Assuming that the image sources meet Gaussian scale mixture model,regarding image fusion proces-sing as image signal distortion channel,and considering human visual noise characteristics,VIF digital image fusion objective quality assessment model was established,and an image fusion quality evaluation index-FVIF was derived also.The experient indicated that the method can evaluate fusion image quality comprehensively and is better than other methods.The results of FVIF method are consistent with the human subjective assessment results.

Key words: Fusion image quality assessment,Visual information fidelity(VIF),Gaussian scale mixture(GSM),Human visual system(HVS)

[1] Wang Z,Bovik A C,Sheikh H R,et al.Image quality assessment:from error measurement to structural similarity[J].IEEE Transactions on Image Processing,2004,13(4):600-612
[2] Xydeas C S,Petrovic V.Objective image fusion performancemeasure[J].Electronics Letters,2000,36(4):308-309
[3] Li Shu-tao,Kang Xu-dong,Hu Jian-wen,et al.Image matching for fusion of multi-focus images in dynamic scenes[J].Information Fusion,2013,14:147-162
[4] Yang C,Zhang J,Wang X,et al.A novel similarity based quality metric for image fusion[J].Information Fusion,2008,9(2):156-160
[5] Sheikh H R,Bovik A C.Image information and visual quality[J].IEEE Trans.Image Process.,2006,5(2):430-444
[6] Wang Z,Bovik A C.A universal image quality index [J].IEEE Signal Processing Letters,2002,9(3):81-83
[7] Portilla J,Strela V,Wainwright M J,et al.Image denoising using scale mixtures of Gaussians in the wavelet domain[J].IEEE Trans.Image Process.,2003,2:1338-1351
[8] Sheikh H R,Bovik A C,de Veciana G.An Information FidelityCriterion for Image Quality Assessment Using Natural Scene Statistics[J].IEEE Trans.Image Process.,2005,14(12):2117-2128
[9] Wang Zhou,Li Qiang.Information Content Weighting for Perceptual Image Quality Assessment[J].IEEE Transactions on Image Processing,2011,20(5):1185-1198

No related articles found!
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
No Suggested Reading articles found!