Digital Image Quality Assessment Based on Standard Normal Deviation

DOI QR코드

DOI QR Code

Park, Hyung-Ju;Har, Dong-Hwan

  • 투고 : 2015.02.25
  • 심사 : 2015.05.07
  • 발행 : 2015.06.28

초록

We propose a new method that specifies objective image quality factors by evaluating an image quality measurement model using random images. In other words, No-Reference variables are used to evaluate the quality of an original image without using any reference for comparison. 1000 portrait images were collected from a web gallery with votes constituting over 30 recommendation values. The bottom-up data collecting process was used to calculate the following image quality factors: total range, average, standard deviation, normalized distribution, z-score, preference percentage. A final grade is awarded out of 100 points, and this method ranks and grades the final estimated image quality preference in terms of total image quality factors. The results of the proposed image quality evaluation model consist of the specific dynamic range, skin tone R, G, B, L, A, B, and RSC contrast. We can present the total for the expected preference points as the average of the objective image qualities. Our proposed image quality evaluation model can measure the preferences for an actual image using a statistical analysis. The results indicate that this is a practical image quality measurement model that can extract a subject's preferred image quality.

키워드

Image Quality;Preference;Objective;Subjective

참고문헌

  1. H. J. Park and D. H. Har, “Subjective Image Quality Assessment based on Objective Image Quality Measurement Factors,” IEEE Transactions on Consumer Electronics, vol. 57, no. 3, Aug. 2011.
  2. S. Luo, P. Etz, A. Singhal, and R. T. Gray, "Performancescalable computational approach to main subject detection in photographs," in Proc. SPIE Conf. on Human Vision and Electronic Imaging, vol. 4299, Jan. 2001, pp. 494-505.
  3. C. D. Cerosaletti, M. E. Miller, and R. A. Drexel, "The perception of depth in photographic images," IS & T;s PICS Conference, Rochester, NY, 2003.
  4. A. C. Gallagher, “A ground truth based vanishing point detection algorithm,” Pattern Recognition, vol. 35, 2002, pp. 1527-1543. https://doi.org/10.1016/S0031-3203(01)00128-5
  5. ISO 14524, Photography-Electronic Still-picture Camerasmethods for measuring Opto- Electronic Conversion Functions (OECFs), International Standard Organization, 2005.
  6. L. Wong and K. Low "Saliency-enhanced image aesthetics class prediction," In Proc. 16th IEEE International Conference on Image Processing, vol. 6077, 2009, pp. 788-799.
  7. X. Jin, M. Zhao, X. Chen, Q. Zhao, and S. Zhu, "Learning Artistic Lighting Template from Portrait Photographs," In Proc. European Conference on Computer Vision ECCV, 2010.
  8. Y. Sohn, Zone system for digital imaging, Chung-Ang University, 2006.
  9. ISO 15739, Photography-Electronic still-picture imaging -Noise measurements, International Standard Organization, 2003.
  10. G. R. Arce, Nonlinear Signal Processing: A Statistical Approach, Wiley: New Jersey, USA, 2005.
  11. A. Torralba, “Contextual priming for object detection,” International Journal of Computer Vision, vol. 53, 2003, pp. 169-191. https://doi.org/10.1023/A:1023052124951
  12. A. Rizzi, G. Simone, and R. Cordone, "A modified algorithm for perceived contrast in digital images," Fourth European Conference on Color in Graphics, Imaging and Vision, 2008, pp.249-252.
  13. G Simone, M Pedersen, and J. Y. Hardeberg, “Measuring perceptual contrast in digital images,” Journal of Visual Communication and Image Representation, vol. 25, issue. 1, Jan. 2012, pp. 491-506. https://doi.org/10.1016/j.jvcir.2012.01.008
  14. C. Cerosaletti and A. C. Loui, "Measuing the perceived aesthetic quality of photographic images," International workshop on quality of multimedia experience, 2009, pp. 47-52.
  15. C. Li, A. Gallagher, A. C. Loui, and T. Chen, "Aesthetic quality assessment of consumer photos with faces," in Proc. 17th IEEE International Conference on Image Processing, 2010, pp. 3221-3224.
  16. W. Luo, X. Wang, and X. Tang, "Content-Based Photo Quality Assessment," In Proc. IEEE International Conference on Computer Vision, 2011, pp. 2206-2213.
  17. T. Leistie, J. Radun, T. Virtanen, R. Halonen, and G. Nyman, "Subjective Experience of Image Quality: Attributes, Definitions and Decision Making of Subjective Image Quality," In Proc. Image Quality and System Performance, SPIE-IS&T Electronic Imaging, SPIE, vol. 7242, 2009.
  18. P. G. Engeldrum, “A Theory of Image Quality: The Image Quality Circle,” Journal of Imaging Science and Technology, vol. 48, no. 5, 2004, pp. 446-456.
  19. A. Zaric, M. Loncaric, D. Tralic, M. Brzica, E. Dumic, and S. Grgic, "Image Quality Assessment - Comparison of Objective Measures with Results of Subjective Test," In Proc. 52nd International Symposium ELMAR, 2010.
  20. R. Datta, D. Joshi, J. Li, and J. Wang, "Studying aesthetics in photographic images using a computational approach," in Proc. 9th European Conference on Computer Vision, Graz, Austria ECCV, 2006.
  21. H. J. Park and D. H. Har, “Optimum Parameter Ranges on Highly Preferred Images: Focus on Dynamic Range, Color, and Contrast,” Korea Contents Association Journal, vol. 13, no. 1, 2013, pp. 9-18. https://doi.org/10.5392/JKCA.2013.13.01.009
  22. K. He, J. Sun, and X. Tang, “Single image haze removal using dark channel prior,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 33, no. 12, 2011, pp. 2341-2353. https://doi.org/10.1109/TPAMI.2010.168
  23. K. He, J. Sun, and X. Tang, "Single image haze removal using dark channel prior," In Proc. IEEE Transactions on Computer Vision and Pattern Recognition, 2010.
  24. R. Datta, J Joshi, J. Li, and J. Z. Wang, "Studying aesthetics in photographic images using a computational approach," in Proc. 9th European Conference on Computer Vision, Graz, Austria, vol. 3953, May 7-13, 2006, pp. 288-301.
  25. Y. Luo and X. Tang, "Photo and video quality evaluation: Focusing on the subject," in Proc. European Conference on Computer Vision, vol. 5304, 2008, pp. 386-399.
  26. R. Datta, J Joshi, J. Li, and J. Z. Wang, "Learning the consensus on visual quality for next-generation image management," in Proc. 15th International Conference on Multimedia, Augsburg, Germany, 2007, pp. 533-536.
  27. Y. Ke, X. Tang and F. Jing, "The design of high-level features for photo quality assessment," in Proc. IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2006, pp. 419-426.
  28. C. Li and T. Chen, "Aesthetic visual quality assessment of paintings," IEEE Journal of Selected Topics in Signal Processing, vol. 3, no. 2, Apr. 2009, pp. 236-252.