  Based on the notion of just noticeable differences (JND), a stair quality
function (SQF) was recently proposed to model human perception on JPEG images.
Furthermore, a k-means clustering algorithm was adopted to aggregate JND data
collected from multiple subjects to generate a single SQF. In this work, we
propose a new method to derive the SQF using the Gaussian Mixture Model (GMM).
The newly derived SQF can be interpreted as a way to characterize the mean
viewer experience. Furthermore, it has a lower information criterion (BIC)
value than the previous one, indicating that it offers a better model. A
specific example is given to demonstrate the advantages of the new approach.
