# Prediction of Satisfied User Ratio for Compressed Video

arXiv: https://arxiv.org/abs/1710.11090 · HTML: https://mlsanity.com/paper/1710.11090/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Oct 2017.

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

- [Perceptual Video Coding for Machines via Satisfied Machine Ratio Modeling](https://mlsanity.com/paper/2211.06797/) (2022)
- [The Visual Experience Dataset: Over 200 Recorded Hours of Integrated Eye Movement, Odometry, and Egocentric Video](https://mlsanity.com/paper/2404.18934/) (2024)

## Abstract (arXiv)

A large-scale video quality dataset called the VideoSet has been constructed recently to measure human subjective experience of H.264 coded video in terms of the just-noticeable-difference (JND). It measures the first three JND points of 5-second video of resolution 1080p, 720p, 540p and 360p. Based on the VideoSet, we propose a method to predict the satisfied-user-ratio (SUR) curves using a machine learning framework. First, we partition a video clip into local spatial-temporal segments and evaluate the quality of each segment using the VMAF quality index. Then, we aggregate these local VMAF measures to derive a global one. Finally, the masking effect is incorporated and the support vector regression (SVR) is used to predict the SUR curves, from which the JND points can be derived. Experimental results are given to demonstrate the performance of the proposed SUR prediction method.