Mobile Video Expeditions

Temporal Quality Assessment for Mobile Videos

Appears In: The 18th Annual International Conference on Mobile Computing and Networking (ACM MOBICOM), 2012 (to appear)

Team: A Chan, A Pande, E. Baik and P Mohapatra

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.

Securing Multimedia Content Using Joint Compression and Encryption

Appears In: IEEE Multimedia (to appear)

Team: A Pande, P Mohapatra and J Zambreno

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. In this work, we show how two compression blocks for video coding: a modified frequency transform (called as Secure Wavelet Transform or SWT) and a modified entropy coding scheme, (called Chaotic Arithmetic Coding (CAC)) can be used for video encryption. Experimental results are shown for selective encryption using proposed schemes.

Investigation of Scalable Video Delivery using H.264 SVC on an LTE Network

Appears In: 14th International Symposium on Wireless Personal Multimedia Communications 2011 (Best Paper Award)

Team: P Mcdonagh, A Pande, C Vallati, P Mohapatra, P A Perry and E Mingozzi

The combination of increased data rates (in 4G cellular networks such as LTE Advanced), dedicated multicast/ broadcast services (e-MBMS), and the emergence of scalable video coding standards (H.264 SVC) allows mobile operators to offer multimedia-based services with a high quality of experience to end users. H.264 SVC offers three dimensions of scalability v.i.z. Quality (SNR), Temporal and Spatial. We present a simulation framework to assess the video quality of scalable video streamed over an LTE network through the use of multiple objective quality metrics such as PSNR, SSIM, Blocking and Blurring. The framework integrates an LTE simulator based on OPNET, combined with quality analysis of H.264/SVC compressed video, using the same metrics as detailed above. We evaluate the performance of scalable video delivery, in both a lossless scenario and in a scenario with packet losses in the LTE network. The results advocate the use of no-reference evaluation metrics along with a frame-drop metric over full-reference metrics which (due to their nature) can’t be used in real-life deployments. We also observe that spatial scalability leads to maximum degradation of image quality compared with temporal and quality scalability.