The new model uses a color space that better matches human color vision compared to the RGB space of the old model, and can therefore extract more meaning from each calibration test.
In an empirical comparison, we found that ICD-2 is 24 times faster than the old approach, and had small but significant gains in accuracy.
This may hinder visual communication owing to the increasing use of colors in recent years.
To address this problem, we re-map the hue components in the HSV color space based on the statistics of local characteristics of the original color image.
This approach works well, but requires repeated calibration – and the best available calibration procedure takes more than 30 minutes.
Recoloring tools exist to reduce the problem, but these tools need a model of the user’s color-differentiation ability in order to work.
In accessibility improvement component, we propose an efficient recoloring algorithm to modify the colors of the images such that they can be better perceived by colorblind users.
We also propose the Accessibility Average Precision (AAP) for AIS as a complementary performance evaluation measure to the conventional relevance-based evaluation methods.
For accessibility assessment, we introduce an analysisbased method and a learning-based method.
Based on the measured accessibility scores, different reranking methods can be performed to prioritize the images with high accessibilities.
The model is based on a short calibration performed by a particular user for a particular display, and so automatically covers all aspects of the user’s ability to see and differentiate colors in an environment.