2017/10/30 by Haiqiang Wang, Ioannis Katsavounidis, Wang, Haiqiang +7 · 1 citation
Computer Science · #Advanced Image Processing Techniques #FOS: Computer and information sciences #Image Enhancement Techniques #Image and Video Quality Assessment #Multimedia (cs.MM)
paper · pdf · doi:10.48550/arxiv.1710.11090
openalex publication_date 2017/10/30 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.