A GMM-Based Stair Quality Model for Human Perceived JPEG Images

TL;DR

This paper introduces a Gaussian Mixture Model-based approach to derive a stair quality function for JPEG images, improving the modeling of human perception compared to previous clustering methods.

Contribution

It proposes a novel GMM-based method for deriving the stair quality function, providing a better fit and interpretation of viewer experience.

Findings

GMM-derived SQF has a lower BIC value than previous methods.

The new approach better characterizes the mean viewer experience.

Demonstrated advantages through a specific example.

Abstract

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.