(Received: 2017-10-17, Revised: 2017-11-30 , Accepted: 2017-12-17)
This study was conducted to explore the role of two color features in improving the performance of the existing No-Reference Image Quality Assessment Algorithms for Contrast-Distorted Images (NR-IQA-CDI). The used color features were Colorfulness and Naturalness of color expressed in CIELab and CIELuv color spaces. Test images used were the public benchmark databases that contain contrast-distorted images - TID2013, CID2013 and CSIQ. Experiments for the exploration were conducted in two stages: the preliminary stage and the comprehensive stage. The results of preliminary study showed that the features of colorfulness and naturalness of color can improve the prediction of human opinion score which relies mainly on the feature of brightness-only contrast. The results inspired to more comprehensive study where the Natural Scene Statistics (NSS) of these two features were estimated by modelling the probability distribution function (pdf) of 16,873 test images from a public database called SUN2012. The results based on k-fold cross validation with k ranging from 2 to 10 showed that the performance of NR-IQA-CDI can be improved by adding the NSS of these features.
  1. Y. Fang, K. Ma, Z. Wang and W. Lin, "No-Reference Quality Assessment of Contrast-Distorted Images Based on Natural Scene Statistics," IEEE Signal Processing Letters, vol. 22, no. 7, pp. 838-842, 2015.
  2. W. Lin and C. -C. J. Kuo, "Perceptual Visual Quality Metrics: A survey," Visual Communication and Image Representation, vol. 22, no. 4, pp. 297-312, 2011.
  3. A. C. B. Zhou Wang, Modern Image Quality Assessment (Synthesis Lectures on Image, Video, & Multimedia Processing), San Mateo, CA, USA: Morgan & Claypool Publishers, 2006.
  4. Z. Wang, A. Bovik, H. Sheikh and E. Simoncelli, "Image Quality Assessment: From Error Visibility to Structural Similarity," IEEE Trans. Image Processing, CA, 2004.
  5. H. R. Sheikh and A. C. Bovik, "Image Information and Visual Quality," Transactions on Image Processing, Montreal, Que., Canada, 2006.
  6. R. Hassen, Z. Wang and M. Salama, "No-Reference Image Sharpness Assessment Based on Local Phase Coherence Measurement," The IEEE International Conference on Acoustics, Speech and Signal Processing, Dallas, TX, USA, 2010.
  7. L. Li, W. Lin, X. Wang, G. Yang, K. Bahrami and A. C. Kot, "No-Reference Image Blur Assessment Based on Discrete Orthogonal Moments," in IEEE Transactions on Cybernetics, Cybern, 2016.
  8. D. L. Ruderman and W. Bialek, "Statistics of Natural Images: Scaling in the Woods," Physical Review Letters, vol. 73, no. 6, pp. 551-558, 1995.
  9. K. Gu, G. Zhai, W. Lin, X. Yang and W. Zhang, "No-Reference Image Sharpness Assessment in Autoregressive Parameter Space," Shanghai Municipal Commission of Economy and Informatization, China, Shanghai, 2015.
  10. J. Shen, Q. Li and G. Erlebacher, "Curvelet-Based No-Reference Objective Image Quality Assessment," Proceedings of the 27th Picture Coding Symposium (PCS'09), NJ, USA, 2009.
  11. W. Lua, K. Zing, D. Tao, Y. Yuan and X. Gao, "No-Reference Image QualityAassessment in Contourlet Domain," Neurocomputing, vol. 73, no. 4-6, pp. 784-794, January 2010.
  12. C. Li, A. C. Bovik and X. Wu, "Blind Image Quality Assessment Using a General Regression Neural Network," IEEE Transactions on Neural Networks, 2011.
  13. A. K. Moorthy and Alan Conrad Bovik, "Blind Image Quality Assessment: From Natural Scene Statistics to Perceptual Quality," IEEE Transactions on Image Processing, 2011.
  14. A. K. Tripathi, S. Mukhopadhyay and A. K. Dhara, "Performance Metrics for Image Contrast," International Conference on Image Information Processing (ICIIP), Shimla, India, Nov. 2011.
  15. A. Mittal, A. K. Moorthy and A. C. Bovik, "No-Reference Image Quality Assessment in the Spatial Domain," IEEE Transactions on Image Processing, August, 2012.
  16. A. Mittal, R. Soundarajan and A. C. Bovik, "Making a “Completely Blind” Image Quality Analyzer," IEEE Signal Processing Letters, pp. 209-212, 2013.
  17. K. Gu, G. Zhai, X. Yang, W. Zhang and M. Liu, "Subjective and Objective Quality Assessment for Images with Contrast Change," Image Processing (ICIP), Melbourne, VIC, Australia, Sept. 2013.
  18. W. Xue, X. Mou, L. Zhang, A. C. Bovik and X. Feng, "Blind Image Quality Assessment Using Joint Statistics of Gradient Magnitude and Laplacian Features," IEEE Transactions on Image Processing, 2014.
  19. B. Liu, L.-X. Liu, H.-P. Dong and Y.-G. Lin, "Blind Image Quality Assessment Based on Mutual Information," Electronics, Communications and Networks, vol. 382, pp. 127-136, 29 June 2016.
  20. H. Sheikh, Z. Wang, L. Cormack and A. Bovik, "LIVE Image Quality Assessment Database Release 2,"[Online]. Available:[Accessed 2014].
  21. K. Gu, G. Zhai, W. Lin and M. Liu, "The Analysis of Image Contrast: From Quality Assessment to Automatic Enhancement," Transactions on Cybernetics (T-)CYP, vol. 46, no. 1, pp. 284 - 297, Jan 2016.
  22. J. Wu, Z. Xia, Y. Ren and H. Li, "No-Reference Quality Assessment for Contrast-Distorted Image," International Conference on Image Processing Theory Tools and Applications (IPTA), Oulu, Finland, Dec. 2016.
  23. K. Gu, W. Lin, G. Zhai, X. Yang, W. Zhang and C. W. Chen, "No-Reference Quality Metric of Contrast-Distorted Images Based on Information Maximization," IEEE Transactions on CYBERNETICS, 2016.
  24. A. Shokrollahi, A. Mahmoudi-Aznaveh and B. M.-N. Maybodi, "Image Quality Assessment for Contrast Enhancement Evaluation," International Journal of Electronics and Communications (AEÜ), pp. 61-66, 2017.
  25. K. Gu, J. Zhou, J.-F. Qiao, G. Zhai, W. Lin and A. C. Bovik, "No-Reference Quality Assessment of Screen Content Pictures," IEEE Transactions on Image Processing, China, Singapore, 2017.
  26. K. Gu, D. Tao, J.-F. Qiao and W. Lin, "Learning a No-Reference Quality Assessment Model of Enhanced Images with Big Data," IEEE Transactions on Neural Networks and Learning Systems, Singapore, 2017.
  27. T. Virtanen, M. Nuutinen, M. Vaahteranoksa, P. Oittinen and J. Häkkinen, "CID2013: A Database for Evaluating No-Reference Image Quality Assessment Algorithms," IEEE Transactions on Image Processing, vol. 24, no. 1, pp. 390-402, 2015.
  28. N. Ponomarenko, O. Ieremeiev, V. Lukin, K. Egiazarian, L. Jin, J. Astola and B. Vozel, "Color Image Database TID2013: Perculiarities and Preliminary Results," The 4th European Workshop on Visual Information, 2013.
  29. E. C. Larson and D. M. Chandler, "Categorical Image Quality (CSIQ) Database," 2010.[Online], Available:
  30. M. Faichild, Color Appearance Models, John Wiley & Sons, p. 87, 2013.
  31. I. R. Assembly, "Methodology for the Subjective Assessment of the Quality of Television Pictures ITU - Radiocom Sector," International Telecommunication Union, 2013.