Comparing subjective image quality measurement methods for the creation of public databases

The Single Stimulus (SS) method is often chosen to collect subjective data testing no-reference objective metrics, as it is straightforward to implement and well standardized. At the same time, it exhibits some drawbacks; spread between different assessors is relatively large, and the measured ratings depend on the quality range spanned by the test samples, hence the results from different experiments cannot easily be merged . The Quality Ruler (QR) method has been proposed to overcome these inconveniences. This paper compares the performance of the SS and QR method for pictures impaired by Gaussian blur. The research goal is, on one hand, to analyze the advantages and disadvantages of both methods for quality assessment and, on the other, to make quality data of blur impaired images publicly available. The obtained results show that the confidence intervals of the QR scores are narrower than those of the SS scores. This indicates that the QR method enhances consistency across assessors. Moreover, QR scores exhibit a higher linear correlation with the distortion applied. In summary, for the purpose of building datasets of subjective quality, the QR approach seems promising from the viewpoint of both consistency and repeatability.

[1]  Ying Chen,et al.  Softcopy quality ruler method: implementation and validation , 2009, Electronic Imaging.

[2]  Allen Parducci,et al.  Category rating scales: Effects of relative spacing and frequency , 1971 .

[3]  Sugato Chakravarty,et al.  Methodology for the subjective assessment of the quality of television pictures , 1995 .

[4]  Nikolay N. Ponomarenko,et al.  Color image database for evaluation of image quality metrics , 2008, 2008 IEEE 10th Workshop on Multimedia Signal Processing.

[5]  Hitoshi Urabe,et al.  ISO 20462: a psychophysical image quality measurement standard , 2003, IS&T/SPIE Electronic Imaging.

[6]  Patrick Le Callet,et al.  Subjective quality assessment IRCCyN/IVC database , 2004 .

[7]  Peter G. Engeldrum,et al.  Psychometric Scaling: A Toolkit for Imaging Systems Development , 2000 .

[8]  Hantao Liu,et al.  A no-reference metric for perceived ringing , 2009 .

[9]  Mauro Barni,et al.  A psychovisual experiment on the use of Gibbs potential for the quality assessment of geometrically distorted images , 2008, Electronic Imaging.

[10]  Vittorio Baroncini New Tendencies in Subjective Video Quality Evaluation , 2006, IEICE Trans. Fundam. Electron. Commun. Comput. Sci..

[11]  Yuukou Horita,et al.  Impact of subjective dataset on the performance of image quality metrics , 2008, 2008 15th IEEE International Conference on Image Processing.

[12]  Huib de Ridder,et al.  Cognitive issues in image quality measurement , 2001, J. Electronic Imaging.

[13]  Brian Keelan,et al.  Handbook of Image Quality: Characterization and Prediction , 2002 .