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A New Preprocessing Method for Measuring Image Visual Quality Robust to Rotation and Spatial Shifts

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Structural, Syntactic, and Statistical Pattern Recognition (S+SSPR 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13813))

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Abstract

Measuring the visual quality of an image is an extremely important task in computer vision. In this paper, we perform 2D fast Fourier transform (FFT) to both test and reference images and take the logarithm of their spectra. We convert both log spectra images to polar coordinate system from cartesian coordinate system and use FFT to extract features that are invariant to translation and rotation. We apply the existing structural similarity (SSIM) index to the two invariant feature images, where no extra inverse transform is needed. Experimental results show that our proposed preprocessing method, when combined with the mean SSIM (MSSIM), performs better than the standard MSSIM significantly in terms of visual quality scores even when no distortions are introduced to the images in the LIVE Image Quality Assessment Database Release 2. In addition, when images are distorted by small spatial shifts and rotations, our new preprocessing step combined with MSSIM still performs better than the standard MSSIM.

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Data Availability Statement

The datasets analysed during the current study are available in [8]. These datasets were derived from the following public domain resources: [LIVE image quality assessment database release 2, http://live.ece.utexas.edu/research/quality].

References

  1. Wang, Z., Bovik, A.C., Sheikh, H.R., Simoncelli, E.P.: Image quality assessment: from error visibility to structural similarity. IEEE Trans. Image Process. 13(4), 600–612 (2004)

    Article  Google Scholar 

  2. Rezazadeh, S., Coulombe, S.: Novel discrete wavelet transform framework for full reference image quality assessment. Signal Image Video Process. 7, 559–573 (2013)

    Article  Google Scholar 

  3. Rezazadeh, S., Coulombe, S.: A novel discrete wavelet domain error-based image quality metric with enhanced perceptual performance. Int. J. Comput. Electr. Eng. 4(3), 390–395 (2012)

    Article  Google Scholar 

  4. Qian, S.E., Chen, G.Y.: Four reduced-reference metrics for measuring hyperspectral images after spatial resolution enhancement. In: ISPRS International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vienna, Austria, 5–7 July 2010, pp. 204–208 (2010)

    Google Scholar 

  5. Keller, K., Averbuch, A., Israeli, M.: Pseudo polar-based estimation of large translations, rotations, and scalings in images. IEEE Trans. Image Process. 14(1), 12–22 (2005)

    Article  MathSciNet  Google Scholar 

  6. Chen, G.Y., Coulombe, S.: An FFT-based visual quality metric robust to spatial shift. In: The 11th International Conference on Information Science, Signal Processing, and their Applications (ISSPA), Montreal, Quebec, Canada, 2–5 July 2012, pp. 372–376 (2012)

    Google Scholar 

  7. Manandhar, P.: Polar To/From Rectangular Transform of Images (2021). MATLAB Central File Exchange. https://www.mathworks.com/matlabcentral/fileexchange/17933-polar-to-from-rectangular-transform-of-images. Accessed 8 Jan 2021

  8. Sheikh, H.R., Wang, Z., Cormack, L., Bovik, A.C.: LIVE image quality assessment database release 2. http://live.ece.utexas.edu/research/quality

  9. Kingsbury, N.G.: Complex wavelets for shift invariant analysis and filtering of signals. J. Appl. Comput. Harmon. Anal. 10(3), 234–253 (2001)

    Article  MathSciNet  MATH  Google Scholar 

  10. Chen, G.Y., Qian, S.E.: Denoising of hyperspectral imagery using principal component analysis and wavelet shrinkage. IEEE Trans. Geosci. Remote Sens. 49(3), 973–980 (2011)

    Article  Google Scholar 

  11. Chen, G.Y., Bui, T.D., Krzyzak, A.: Filter-based face recognition under varying illumination. IET Biom. 7(6), 628–635 (2018)

    Article  Google Scholar 

  12. Chen, G.Y., Bui, T.D., Krzyzak, A.: Illumination invariant face recognition using dual-tree complex wavelet transform in logarithm domain. J. Electr. Eng. 70(2), 113–121 (2019)

    Google Scholar 

  13. Chen, G.Y., Sun, W., Xie, W.F.: Hyperspectral face recognition using log-polar Fourier features and collaborative representation-based voting classifiers. IET Biom. 6(1), 36–42 (2017)

    Article  Google Scholar 

  14. Chen, G.Y., Li, C.J., Sun, W.: Hyperspectral face recognition via feature extraction and CRC-based classifier. IET Image Proc. 11(4), 266–272 (2017)

    Article  Google Scholar 

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Authors and Affiliations

  1. Department of Computer Science and Software Engineering, Concordia University, Montreal, QC, H3G 1M8, Canada

    Guang Yi Chen & Adam Krzyzak

  2. Institute of Mathematics and Informatics, Bulgarian Academy of Sciences, 1113, Sofia, Bulgaria

    Ventzeslav Valev

Authors
  1. Guang Yi Chen
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  2. Adam Krzyzak
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  3. Ventzeslav Valev
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Corresponding author

Correspondence to Adam Krzyzak .

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Editors and Affiliations

  1. Concordia University, Montreal, QC, Canada

    Adam Krzyzak

  2. Concordia University, Montreal, QC, Canada

    Ching Y. Suen

  3. Ca Foscari University of Venice, Venezia, Italy

    Andrea Torsello

  4. Concordia University, Montreal, QC, Canada

    Nicola Nobile

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Chen, G.Y., Krzyzak, A., Valev, V. (2022). A New Preprocessing Method for Measuring Image Visual Quality Robust to Rotation and Spatial Shifts. In: Krzyzak, A., Suen, C.Y., Torsello, A., Nobile, N. (eds) Structural, Syntactic, and Statistical Pattern Recognition. S+SSPR 2022. Lecture Notes in Computer Science, vol 13813. Springer, Cham. https://doi.org/10.1007/978-3-031-23028-8_10

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  • DOI: https://doi.org/10.1007/978-3-031-23028-8_10

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-23027-1

  • Online ISBN: 978-3-031-23028-8

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