Subjective image-quality estimation based on psychophysical experimentation

Authors:
Gi-Yeong Gim;Hyunchul Kim;Jin-Aeon Lee;Whoi-Yul Kim
Affiliations:
Department of Electronics and Computer Engineering, Hanyang University, Seoul, Korea;Department of Electronics and Computer Engineering, Hanyang University, Seoul, Korea;Samsung Electronics, Yongin-Si, Gyeonggi-Do, Korea;Department of Electronics and Computer Engineering, Hanyang University, Seoul, Korea
Venue:
PSIVT'07 Proceedings of the 2nd Pacific Rim conference on Advances in image and video technology
Year:
2007

Citing 5
Cited 0

Image dynamic range enhancement and stabilization in the context of the logarithmic image processing model

Signal Processing
Digital Image Processing Using MATLAB

Digital Image Processing Using MATLAB
The Design of High-Level Features for Photo Quality Assessment

CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
Subjective Multimedia Quality Assessment

IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences
Experimental approach for human perception based image quality assessment

ICEC'06 Proceedings of the 5th international conference on Entertainment Computing

Quantified Score

Hi-index	0.00

Visualization

Abstract

The purpose of estimating subjective image quality is to provide best-quality image content to users in diverse fields. Image quality is subjective, therefore very difficult to estimate accurately, and so many researchers have proposed psychophysical experimentation as a means of estimating it. Conventional methods describe the relationship between the subjective preference and the perceived contrast as an "inverted U" shape. However, the relationship was resulted from only a few, high-quality images. Thus, they are inadequate for general image-quality estimation. In this paper, we carried out two experiments using a dataset with untransformed and various images. We discovered an important property that the preference increases in proportion to the perceived contrast. The result shows us not only that our experimentation can reduce MSE of image-quality estimation by approximately 40% over the previous methods, but also that it can be applied in various applications.