Toward a Blind Quality Metric for Temporally Distorted Streaming Video

With the rapid progress of mobile Internet, the streaming video service has boom over wireless networks in recent years. A smooth playback experience is crucial for the popularization of these services. However, limited by fluctuating bandwidth and various network impairments, the streaming video inevitably suffers kinds of stalling events, which significantly distorts its temporal structures and results in annoying jerky playback. In this paper, we propose an efficient quality metric to blindly evaluate the user experience for stalled streaming video without using its original sequence. Instead of requiring buffer or manifest information like existing methods, we only access to the decoded video and extract two complementary image features, i.e., global intensity and local texture, to estimate the stall number and duration. Then, by means of a straightforward and easy-to-use linear combination model, we can map the normalized stall number and duration information to a quantitative quality score. Experimental results on the publicly available LIVE-Avvasi mobile video database show that our predicted video quality is highly consistent with the user experience and outperforms many existing quality-of-experience models.