Laser & Optoelectronics Progress, Volume. 57, Issue 18, 181021(2020)

Quality Assessment of Blind Color Images Using Quaternion Fourier Transform

Ke Zhou1、*, Chengmao Wu2, and Changxing Li3
Author Affiliations
  • 1School of Communication and Information Engineering, Xi'an University of Post and Telecommunications, Xi'an, Shaanxi 710121, China
  • 2School of Electronic Engineering, Xi'an University of Post and Telecommunications, Xi'an, Shaanxi 710121, China
  • 3School of Science, Xi'an University of Post and Telecommunications, Xi'an, Shaanxi 710121, China
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    Figures & Tables(17)
    Original images and spectra of images from R、G、B channels obtained by quaternion Fourier transform. (a)(e) Original images; (b)(f) images from R channel; (c)(g) images from G channel; (d)(h) images from B channel
    Monarch Gaussian blurred images under different standard deviations. (a) σ=0.5; (b) σ=1.0; (c) σ=1.5; (d) σ=2.0; (e) σ=2.5; (f) σ=3.0; (g) σ=3.5; (h) σ=4.0; (i) σ=4.5
    Quaternion Fourier transform spectra of Monarch blurred image under different standard deviations. (a) σ=0.5; (b) σ=1.0; (c) σ=1.5; (d) σ=2.0; (e) σ=2.5; (f) σ=3.0; (g) σ=3.5; (h) σ=4.0; (i) σ=4.5; (j) σ=5.0
    Principle of color image quality assessment
    Building blurred images and their corresponding spectra. (a)(c) σ=0.5; (b)(d) σ=5.0
    Original color images. (a) Bike; (b) building; (c) cap; (d) monarch butterfly; (e) painted house; (f) parrot
    Noiseless blurred images. (a)-(c) σ=5.0; (d)-(f) ρ=20
    Quality assessment results of noiseless Gaussian blurred images
    Quality assessment results of noiseless motion blurred images
    Noisy blurred images. (a)-(c) v=0.02; (d)-(f) d=0.20
    Quality assessment results of images with Gaussian white noises (v=0.01)
    Quality assessment results of images with Gaussian white noises (v=0.02)
    Quality assessment results of images with salt and pepper noises (d=0.10)
    Quality assessment results of images with salt and pepper noises (d=0.20)
    • Table 1. Assessment results

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      Table 1. Assessment results

      σTthresTHSscore
      0.5132.399437830.0096
      5.0130.94537380.0019
    • Table 2. Performance comparison among algorithms on IVC, TID2013 and CSIQ databases

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      Table 2. Performance comparison among algorithms on IVC, TID2013 and CSIQ databases

      DatabaseParameterCPBDENIQAGSVDJNBMLPC-SIQFTM
      PLCC0.95190.48430.79450.93680.97880.9230
      IVC databaseSRCC0.86630.25340.93660.89650.96220.9252
      KRCC0.70970.16130.79040.74200.87100.7742
      RMSE0.34980.99880.69330.39940.23360.4393
      PLCC0.89580.85580.81690.91420.92290.9480
      TID2013 databaseSRCC0.90500.86180.83580.92130.92640.9437
      KRCC0.72210.68740.63090.75660.76660.7966
      RMSE0.55460.64550.71970.50560.48050.3973
      PLCC0.89330.88960.87620.88880.89410.9463
      CSIQ databaseSRCC0.94260.88920.87660.86030.95390.9514
      KRCC0.81150.70510.68940.72370.83220.8178
      RMSE0.12880.13090.13810.13130.12840.0926
    • Table 3. Average running time of methods

      View table

      Table 3. Average running time of methods

      MethodCPBDENIQAGSVDJNBMLPC-SIQFTM
      Running time /s0.140413.11120.46760.71230.79560.0791
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    Ke Zhou, Chengmao Wu, Changxing Li. Quality Assessment of Blind Color Images Using Quaternion Fourier Transform[J]. Laser & Optoelectronics Progress, 2020, 57(18): 181021

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    Paper Information

    Category: Image Processing

    Received: Jan. 7, 2020

    Accepted: Feb. 24, 2020

    Published Online: Sep. 2, 2020

    The Author Email: Zhou Ke (rudygoodman@163.com)

    DOI:10.3788/LOP57.181021

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