Ke Zhou, Chengmao Wu, Changxing Li. Quality Assessment of Blind Color Images Using Quaternion Fourier Transform[J]. Laser & Optoelectronics Progress, 2020, 57(18): 181021
- Laser & Optoelectronics Progress
- Vol. 57, Issue 18, 181021 (2020)
Fig. 1. 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
Fig. 2. 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
Fig. 3. 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
Fig. 4. Principle of color image quality assessment
Fig. 5. Building blurred images and their corresponding spectra. (a)(c) σ=0.5; (b)(d) σ=5.0
Fig. 6. Original color images. (a) Bike; (b) building; (c) cap; (d) monarch butterfly; (e) painted house; (f) parrot
Fig. 7. Noiseless blurred images. (a)-(c) σ=5.0; (d)-(f) ρ=20
Fig. 8. Quality assessment results of noiseless Gaussian blurred images
Fig. 9. Quality assessment results of noiseless motion blurred images
Fig. 10. Noisy blurred images. (a)-(c) v=0.02; (d)-(f) d=0.20
Fig. 11. Quality assessment results of images with Gaussian white noises (v=0.01)
Fig. 12. Quality assessment results of images with Gaussian white noises (v=0.02)
Fig. 13. Quality assessment results of images with salt and pepper noises (d=0.10)
Fig. 14. Quality assessment results of images with salt and pepper noises (d=0.20)
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Table 1. Assessment results
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Table 2. Performance comparison among algorithms on IVC, TID2013 and CSIQ databases
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Table 3. Average running time of methods
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