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

arXiv: https://arxiv.org/abs/1511.03398 · HTML: https://mlsanity.com/paper/1511.03398/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Nov 2015.

## Built on by (most cited first, 2 of 2)

- [Convex Hull Prediction for Adaptive Video Streaming by Recurrent Learning](https://mlsanity.com/paper/2206.04877/) (2022)
- [SG-JND: Semantic-Guided Just Noticeable Distortion Predictor For Image Compression](https://mlsanity.com/paper/2408.04273/) (2024)

## Abstract (arXiv)

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.