an Le-le, Li Hui, Qiu Ju-neng, Liang Ping. Image quality assessment method based on regional contrast and structural similarity[J]. Journal of Applied Optics, 2015, 36(1): 58-63. DOI: 10.5768/JAO201536.0102002
Citation: an Le-le, Li Hui, Qiu Ju-neng, Liang Ping. Image quality assessment method based on regional contrast and structural similarity[J]. Journal of Applied Optics, 2015, 36(1): 58-63. DOI: 10.5768/JAO201536.0102002
shu

Image quality assessment method based on regional contrast and structural similarity

  • 1.

    School of Aeronautic and Astronautic,University of Electronic Science and Technology of China,Chengdu 611731,China;

  • 2.

    Chengdu Entry-Exit Inspection and Quarantine Bureau,Chengdu 610041,China

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  • Received Date: August 24, 2014
  • Revised Date: November 04, 2014
    Graphical Abstract
    • Abstract
  • Among numerous image quality assessment(IQA) methods, the structural similarity (SSIM) algorithm is simple, high efficient and accurate. However, it often does not work well when there is regional distortion or cross distortion in the image. To deal with the problem that SSIM algorithm treats the different regions of the image identically, we took human visual characteristics in spatial domain into consideration and put forward an improved IQA method based on regional contrast and structural similarity(RCSSIM). The new algorithm combines regional contrast with structural similarity, weighs and normalizes the original SSIM index to a regional contrast structural similarity metric between the reference image and the distortion image to assess the image quality. The experiment results on LIVE image database show that the Pearson linear correlation coefficient(PLCC) of the new algorithm increases by about 0.015 and the root-mean-square error decreases by about 0.55 compared with the SSIM algorithm. It indicates that the evaluation result of RCSSIM algorithm is more consistent with human visual system(HVS) characteristics and is more effective than the SSIM algorithm.
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    • References (1)
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