Prediction of Satisfied User Ratio for Compressed Video
This work addresses video quality assessment for users by predicting SUR curves, but it is incremental as it builds on existing datasets and methods.
The authors tackled the problem of predicting satisfied-user-ratio (SUR) curves for compressed video by developing a machine learning method that uses VMAF quality indices and support vector regression, achieving performance demonstrated through experimental results.
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.