Motion tuned spatio-temporal quality assessment of natural videos
IEEE Transactions on Image Processing
Quadrant of euphoria: a crowdsourcing platform for QoE assessment
IEEE Network: The Magazine of Global Internetworking - Special issue on improving quality of experience for network services
Metrics for evaluating video streaming quality in lossy IEEE 802.11 wireless networks
INFOCOM'10 Proceedings of the 29th conference on Information communications
Design of integrated multimedia compression and encryption systems
IEEE Transactions on Multimedia
Perceptual Temporal Quality Metric for Compressed Video
IEEE Transactions on Multimedia
Image quality assessment: from error visibility to structural similarity
IEEE Transactions on Image Processing
VSNR: A Wavelet-Based Visual Signal-to-Noise Ratio for Natural Images
IEEE Transactions on Image Processing
Efficient Video Quality Assessment Along Temporal Trajectories
IEEE Transactions on Circuits and Systems for Video Technology
Context-aware frame rate adaption for video chat on smartphones
Proceedings of the 2013 ACM conference on Pervasive and ubiquitous computing adjunct publication
Evaluating perceptual video quality for mobile clients in 802.11n WLAN
Proceedings of the 8th ACM international workshop on Wireless network testbeds, experimental evaluation & characterization
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Video quality assessment in mobile devices, for instances smart phones and tablets, raises unique challenges such as unavailability of original videos, the limited computation power of mobile devices and inherent characteristics of wireless networks (packet loss and delay). In this paper, we present a metric, Temporal Variation Metric (TVM), to measure the temporal information of videos. Despite its simplicity, it shows a high correlation coefficient of 0.875 to optical flow which captures all motion information in a video. We use the TVM values to derive a reduced-reference temporal quality assessment metric, Temporal Variation Index (TVI), which quantifies the quality degradation incurred in network transmission. Subjective assessments demonstrate that TVI is a very good predictor of users' Quality of Experience (QoE). Its prediction shows a 92.5% of correlation to subjective Mean Opinion Score (MOS) ratings. Through video streaming experiments, we show that TVI can also estimate the network conditions such as packet loss and delay. It depicts an accuracy of almost 95% in extensive tests on 183 video traces.