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Conventional telepresence systems allow remote users to see one another and interact with shared media, but users cannot make eye contact, and gaze awareness with respect to shared media and documents is lost. In this paper, we describe a remote collaboration system based on a see-through display to create an experience where local and remote users are seemingly separated only by a vertical sheet of glass. Users can see each other and media displayed on the shared surface. Face detectors are applied on the local and remote video streams to introduce an offset in the video display so as to bring the local user's face, the local camera, and the remote user's face image into collinearity. This ensures that, when the local user looks at the remote user's image, the camera behind the see-through display captures an image with the “Mona Lisa effect,” where the eyes of an image appears to follow the viewer. Experiments show that, for one-on-one meetings, our system is capable of capturing and delivering realistic, genuine eye contact as well as accurate gaze awareness with respect to shared media. [ABSTRACT FROM PUBLISHER]

This paper investigates the problem of assessing the quality of video transmitted over IP networks. Our goal is to develop a methodology that is both reasonably accurate and simple enough to support the large-scale deployments that the increasing use of video over IP are likely to demand. For that purpose, we focus on developing an approach that is capable of mapping network statistics, e.g., packet losses, available from simple measurements, to the quality of video sequences reconstructed by receivers. A first step in that direction is a loss-distortion model that accounts for the impact of network losses on video quality, as a function of application-specific parameters such as video codec, loss recovery technique, coded bit rate, packetization, video characteristics, etc. The model, although accurate, is poorly suited to large-scale, on-line monitoring, because of its dependency on parameters that are difficult to estimate in real-time. As a result, we introduce a "relative quality" metric (rPSNR) that bypasses this problem by measuring video quality against a quality benchmark that the network is expected to provide. The approach offers a lightweight video quality monitoring solution that is suitable for large-scale deployments. We assess its feasibility and accuracy through extensive simulations and experiments. [ABSTRACT FROM AUTHOR]

The need for security services, such as confidentiality and authentication, has become one of the major concerns in multimedia communication applications, such as video on demand and peer-to-peer content delivery. Conventional data authentication cannot be directly applied for streaming media when an unreliable channel is used and packet loss may occur. This paper begins by reviewing existing end-to-end media authentication schemes, which can be classified into stream-based and content-based techniques. We then motivate and describe how to design authentication schemes for multimedia delivery that exploit the unequal importance of different packets. By applying conventional cryptographic hashes and digital signatures to the media packets, the system security is similar to that achievable in conventional data security. However, instead of optimizing packet verification probability, we optimize the quality of the authenticated media, which is determined by the packets that are received and able to be decoded and authenticated. The quality of the authenticated media is optimized by allocating the authentication resources unequally across streamed packets based on their relative importance, thereby providing unequal authenticity protection. The effectiveness of this approach is demonstrated through experimental results on different media types (image and video), different compression The need for security services, such as confidentiality and authentication, has become one of the major concerns in multimedia communication applications, such as video on demand and peer-to-peer content delivery. Conventional data authentication cannot be directly applied for streaming media when an unreliable channel is used and packet loss may occur. This paper begins by reviewing existing end-to-end media authentication schemes, which can be classified into stream-based and content-based techniques. We then motivate and describe how to design authentication schemes for multimedia delivery that exploit the unequal importance of different packets. By applying conventional cryptographic hashes and digital signatures to the media packets, the system security is similar to that achievable in conventional data security. However, instead of optimizing packet verification probability, we optimize the quality of the authenticated media, which is determined by the packets that are received and able to be decoded and authenticated. The quality of the authenticated media is optimized by allocating the authentication resources unequally across streamed packets based on their relative importance, thereby providing unequal authenticity protection. The effectiveness of this approach is demonstrated through experimental results on different media types (image and video), different compression standards (JPEG, JPEG2000, and H.264), and different channels (wired with packet erasures and wireless with bit errors). [ABSTRACT FROM AUTHOR]

Recent advances in imaging sensor technology make high frame-rate video capture practical. As demonstrated in previous work, this capability can be used to enhance the performance of many image and video processing applications. The idea is to use the high frame-rate capability to temporally oversample the scene and, thus, to obtain more accurate information about scene motion and illumination. This information is then used to improve the performance of image and standard frame-rate video applications. This paper investigates the use of temporal oversampling to improve the accuracy of optical flow estimation (OFE). A method for obtaining high accuracy optical flow estimates at a conventional standard frame rate, e.g., 30 frames/s, by first capturing and processing a high frame-rate version of the video is presented. The method uses the Lucas-Kanade algorithm to obtain optical flow estimates at a high frame rate, which are then accumulated and refined to estimate the optical flow at the desired standard frame rate. The method demonstrates significant improvements in OFE accuracy both on synthetically generated video sequences and on a real video sequence captured using an experimental high-speed imaging system. It is then shown that a key benefit of using temporal oversampling to estimate optical flow is the reduction in motion aliasing. Using sinusoidal input sequences, the reduction in motion aliasing is identified and the desired minimum sampling rate as a function of the velocity and spatial bandwidth of the scene is determined. Using both synthetic and real video sequences, it is shown that temporal oversampling improves OFE accuracy by reducing motion aliasing not only for areas with large displacements but also for areas with small displacements and high spatial frequencies. The use of other OFE algorithms with temporally over-sampled video is then discussed. In particular, the Haussecker algorithm is extended to work with high frame-rate sequences. This extension demonstrates yet another important benefit of temporal oversampling, which is improving OFE accuracy when brightness varies with time. [ABSTRACT FROM AUTHOR]

The article discusses various reports published within the issue, including one by Nilanjan Banerjee, Arup Acharya and Sajal K. Das on the use of the Session Initiation Protocol to support seamless mobility for multimedia applications and another by Hrvoje Jenkac, Thomas Stockhammer, and Wen Xu on the scalable wireless broadcasting system that support heterogeneous receivers.

Recent advances in imaging sensor technology make high frame-rate video capture practical. As demonstrated in previous work, this capability can be used to enhance the performance of many image and video processing applications. The idea is to use the high frame-rate capability to temporally oversample the scene and, thus, to obtain more accurate information about scene motion and illumination. This information is then used to improve the performance of image and standard frame-rate video applications. This paper investigates the use of temporal oversampling to improve the accuracy of optical flow estimation (OFE). A method for obtaining high accuracy optical flow estimates at a conventional standard frame rate, e.g., 30 frames/s, by first capturing and processing a high frame-rate version of the video is presented. The method uses the Lucas-Kanade algorithm to obtain optical flow estimates at a high frame rate, which are then accumulated and refined to estimate the optical flow at the desired standard frame rate. The method demonstrates significant improvements in OFE accuracy both on synthetically generated video sequences and on a real video sequence captured using an experimental high-speed imaging system. It is then shown that a key benefit of using temporal oversampling to estimate optical flow is the reduction in motion aliasing. Using sinusoidal input sequences, the reduction in motion aliasing is identified and the desired minimum sampling rate as a function of the velocity and spatial bandwidth of the scene is determined. Using both synthetic and real video sequences, it is shown that temporal oversampling improves OFE accuracy by reducing motion aliasing not only for areas with large displacements but also for areas with small displacements and high spatial frequencies. The use of other OFE algorithms with temporally oversampled video is then discussed. In particular, the Haussecker algorithm is extended to work with high frame-rate sequences. This extension demonstrates yet another important benefit of temporal oversampling, which is improving OFE accuracy when brightness varies with time.