In this article, we explore the possibility of enabling cloud-based virtual space applications for better computational scalability and easy access from any end device, including future lightweight wireless head-mounted displays. In particular, we investigate virtual space applications such as virtual classroom and virtual gallery, in which the scenes and activities are rendered in the cloud, with multiple views captured and streamed to each end device. A key challenge is the high bandwidth requirement to stream all the user views, leading to high operational cost and potential large delay in a bandwidth-restricted wireless network. We propose a novel hybrid-cast approach to save bandwidth in a multi-user streaming scenario. PubDate: Fri, 15 Jun 2018 00:00:00 GMT
Abstract: Chang Liu, Wei Tsang Ooi, Jinyuan Jia, Lei Zhao
We propose Cloud Baking, a collaborative rendering architecture for dynamic Web3D scenes. In our architecture, the cloud renderer renders the scene with the global illumination (GI) information in a GI map; the web-based client renderer renders the scene with ambient lighting only and blends it with the GI map received from the cloud for the final scene. This approach allows the users to interact with the web scene and change the scene dynamically through the web interface end, yet move the computationally heavy tasks of global illumination computation to the cloud. A challenge we face is the interaction delay that causes the frames rendered on the cloud and the client to go out of sync. PubDate: Fri, 15 Jun 2018 00:00:00 GMT
Abstract: Chao Zhou, Zhenhua Li, Joe Osgood, Yao Liu
A new generation of video streaming technology, 360-degree video, promises greater immersiveness than standard video streams. This level of immersiveness is similar to that produced by virtual reality devices—users can control the field of view using head movements rather than needing to manipulate external devices. Although 360-degree video could revolutionize the streaming experience, its large-scale adoption is hindered by a number of factors: 360-degree video streams have larger bandwidth requirements and require faster responsiveness to user inputs, and users may be more sensitive to lower quality streams. In this article, we review standard approaches toward 360-degree video encoding and compare these to families of approaches that distort the spherical surface to allow oriented concentrations of the 360-degree view. PubDate: Fri, 15 Jun 2018 00:00:00 GMT
We show how to build the components of a privacy-aware, live video analytics ecosystem from the bottom up, starting with OpenFace, our new open-source face recognition system that approaches state-of-the-art accuracy. Integrating OpenFace with interframe tracking, we build RTFace, a mechanism for denaturing video streams that selectively blurs faces according to specified policies at full frame rates. This enables privacy management for live video analytics while providing a secure approach for handling retrospective policy exceptions. PubDate: Fri, 15 Jun 2018 00:00:00 GMT
Abstract: Gylfi Þór Guđmundsson, Björn Þór Jónsson, Laurent Amsaleg, Michael J. Franklin
The world has experienced phenomenal growth in data production and storage in recent years, much of which has taken the form of media files. At the same time, computing power has become abundant with multi-core machines, grids, and clouds. Yet it remains a challenge to harness the available power and move toward gracefully searching and retrieving from web-scale media collections. Several researchers have experimented with using automatically distributed computing frameworks, notably Hadoop and Spark, for processing multimedia material, but mostly using small collections on small computing clusters. In this article, we describe a prototype of a (near) web-scale throughput-oriented MM retrieval service using the Spark framework running on the AWS cloud service. PubDate: Fri, 15 Jun 2018 00:00:00 GMT
Hybrid journal (It can contain Open Access articles)
[45 journals]
