On Evaluating Perceptual Quality of Online User-Generated Videos

TL;DR

This paper investigates how viewers perceive the quality of online user-generated videos, evaluates existing quality metrics, and explores the use of metadata for improving quality assessment in video-sharing platforms.

Contribution

It provides a comprehensive analysis of viewer perception patterns, assesses current objective metrics, and examines metadata's potential for enhancing quality evaluation.

Findings

Viewer perception patterns analyzed via graph techniques.

Existing metrics have limitations in UGV quality estimation.

Metadata shows promise for improving quality assessment.

Abstract

This paper deals with the issue of the perceptual quality evaluation of user-generated videos shared online, which is an important step toward designing video-sharing services that maximize users' satisfaction in terms of quality. We first analyze viewers' quality perception patterns by applying graph analysis techniques to subjective rating data. We then examine the performance of existing state-of-the-art objective metrics for the quality estimation of user-generated videos. In addition, we investigate the feasibility of metadata accompanied with videos in online video-sharing services for quality estimation. Finally, various issues in the quality assessment of online user-generated videos are discussed, including difficulties and opportunities.