This paper proposes a novel prediction scheme for depth map coding. We utilize the fact that depth values are largely dependent on objects and one small block consists of only a small number of objects at most. The proposed method approximates each block with one palette and one object shape map. The palette consists of two representative depth values for foreground object and background object in the target block. The object shape map expresses which object exists at each pixel. These data are encoded only at the blocks where the proposed method is used. In order to reduce the bitrates required to transfer palettes and object shape maps, the proposed method enables their prediction by utilizing spatial and temporal correlations. Experiments are conducted on three kinds of depth maps; estimated from multiview images, captured by special sensors, and generated by computer. The results show that the proposed method reduces the bitrate by up to 40% and about 20% on average for 13 sequences relative to the MPEG-4 AVC/H.264.

Many new applications are being created around the panoramic video service. The typical system divides the high resolution panoramic video into tiles and the sender transmits a set of tiles, the partial panoramic video. Coding each tile at a uniform bitrate yields poor video quality because each tile has different visual characteristics. This paper proposes a new data format and tile adaptive rate control to achieve high quality partial panoramic video transmission, even over restricted bandwidth networks. The proposed data format, based on the MVC standard, has two types of video stream and meta data. Each tile is encoded at multiple bitrates and multiplexed synchronously. The meta data has quality values of each tile at the multi-bitrates, and is used to determine the view_ids associated with the bitrates and user's desired view. Our tile-adaptive rate control proposal maximizes the partial panoramic video quality even in restricted bandwidth networks. An experiment shows that the proposed method can achieve higher video quality. The method ensures that the facial elements in the user's view, which often exhibit the greatest motion and to which we are most sensitive, have high quality.

We propose a system that allows multiple groups of users at remote locations to naturally share a 3D image of real objects. All users can interactively observe a 3D stereoscopic image without distortion from their own viewpoints. The system basically consists of a combination of systems: imaging and display. The imaging system captures the real object's images from certain number of viewpoints which are used to generate stereoscopic images from arbitrary viewpoints based on an image-based rendering technique implemented on GPU. The display system interactively generates a 3D virtual image of the real object based on the IllusionHole technique. Users at one place just put the real object on their imaging system to capture a set of its images from sparse viewpoints around it; other groups of multiple users at remote places connected by networks observe its virtual image from arbitrary viewpoints on their display systems.

InterSpace is a revolutionary communication environment that allows users the flexibility of multi-modal interaction. People in InterSpace communicate using audio as well as video interaction in a three dimensional world. Remote terimals are connected to a central server via networks. Facial image, audio, and proximity, are processed and sent out to the remote terminals to enable multi-modal communication in a virtual world. InterSpace technology comes a step closer to bridging the gap between virtual reality and world experiences. We conducted a trial service, CyberCampus, based on the InterSpace platform. Individual actions can now be shared with other users as you explore, talk, shop, learn, and experience the many facets of CyberCampus. Environments related to entertainment, distance learning, on-line shopping, and advertisement are currently being explored in CyberCampus with unlimited expansion capabilities.