Subjective and Objective Evaluation of Multi-exposure High Dynamic Range Image Deghosting Methods
To avoid motion artefacts when merging multiple exposures into an HDR image, a number of deghosting algorithms have been proposed. These algorithms, however, do not work equally well on all types of scenes, and some may even introduce additional artefacts. Even though subjective methods of evaluation provide reliable means of testing, they need to be repeated for each new proposed method or even its slight modification and are cumbersome to perform. In this work, we evaluate several computational approaches of quantitative evaluation of multi-exposure HDR deghosting algorithms and demonstrate their results on five state- of-the-art algorithms. The quality of HDR images produced by deghosting methods is measured in a subjective experiment, and then evaluated using five objective metrics. The most reliable metric is then selected by testing correlation between subjective and objective metric scores.