GazeStereo3D: seamless disparity manipulations

Producing a high quality stereoscopic impression on current displays is a challenging task. The content has to be carefully prepared in order to maintain visual comfort, which typically affects the quality of depth reproduction. In this work, we show that this problem can be significantly alleviated when the eye fixation regions can be roughly estimated. We propose a new method for stereoscopic depth adjustment that utilizes eye tracking or other gaze prediction information. The key idea that distinguishes our approach from the previous work is to apply gradual depth adjustments at the eye fixation stage, so that they remain unnoticeable. To this end, we measure the limits imposed on the speed of disparity changes in various depth adjustment scenarios, and formulate a new model that can guide such seamless stereoscopic content processing. Based on this model, we propose a real-time controller that applies local manipulations to stereoscopic content to find the optimum between depth reproduction and visual comfort. We show that the controller is mostly immune to the limitations of low-cost eye tracking solutions. We also demonstrate benefits of our model in off-line applications, such as stereoscopic movie production, where skillful directors can reliably guide and predict viewers' attention or where attended image regions are identified during eye tracking sessions. We validate both our model and the controller in a series of user experiments. They show significant improvements in depth perception without sacrificing the visual quality when our techniques are applied

A Framework for Cost-Effective Peer-to-Peer Content Distribution

Motivated by the success of the peer-to-peer (P2P) paradigm in the last few years and by the immense number of the often underutilized end systems connected to the Internet, we propose a collaborative P2P framework for cost-effective content distribution. We focus mainly on the problem of streaming large media files (e.g., movies) to a large-scale user community. The key idea of our approach is: instead of deploying powerful caches at many locations, the P2P model relies on resource contributions from peers (client machines). Every peer may contribute a little, but there is an enormous number of them. As peers contribute resources, the overall system capacity increases and more clients can be served. By properly motivating peers to share some of their resources, the system achieves significant cost-effectiveness. The P2P approach strives to push the contents even closer to the clients: contents are obtained from fellow peers within the same network domain. This potentially can lead to..