Mobile Video Expeditions

Current Research Thrusts:

Live Video Forensics
Video source identification is very important in validating video evidence, tracking down video piracy crimes and regulating individual video sources. With the prevalence of wireless communication, wireless video cameras continue replacing their wired counterparts in security / surveillance systems and tactical networks. However, wirelessly streamed videos usually suffer from blocking and blurring due to inevitable packet loss in wireless transmissions. The existing source identification methods experience significant performance degradation or even fail to work when identifying videos with blocking and blurring. In this work, we propose a method which is effective and efficient in identifying such wirelessly streamed videos. In addition, we also propose to incorporate wireless channel signatures and selective frame processing into source identification, which significantly improve the identification speed. We conduct extensive real-world experiments to validate our method. The results show that it largely outperforms the existing methods in the presence of video blocking and blurring. Moreover, with a number of optimizations, our method is able to identify the video source in a near-real-time fashion, which can be used to detect the wireless camera spoofing attack.

Quality of User Experience in Mobile Videos
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 wire- less networks (packet loss and delay). We present a metric, Temporal Variation Metric (TVM), to measure the temporal information of videos. Despite its sim- plicity, it shows a high correlation coefficient of 0.875 to op- tical flow which captures all motion information in a video. We use the TVM values to derive a reduced-reference tem- poral 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 Ex- perience (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 es- timate the network conditions such as packet loss and delay. It depicts an accuracy of almost 95% in extensive tests on 183 video traces. In next stage, we propose to combine TVI with spatial metrics and deploy them in real-world scenarios.

Scheduling and Delivery in 4G LTE Networks
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Multimedia Communications in Wireless Networks
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Multimedia Security for Embedded Devices
Algorithmic parameterization and hardware architectures can ensure secure transmission of multimedia data in resource-constrained environments such as wireless video surveillance networks, tele-medicine frameworks for distant health care support in rural areas, and Internet video streaming. Joint multimedia compression and encryption techniques can significantly reduce the computational requirements of video processing systems. We present an approach to reduce the computational cost of multimedia encryption, while also preserving the properties of compressed video (useful for scalability, transcoding, and retrieval), which endanger loss by naive encryption. Hardware-amenable design of proposed algorithms makes them suitable for realtime embeddedmultimedia systems. This approach alleviates the need of additional hardware for encryption in resourceconstrained scenario, and can be otherwise used to augment existing encryption methods used for content delivery in Internet or other applications.