Ubiquitous is one of the essential features of what should be the desktop of the future. In practice, this concept covers several issues related to multi-users collaboration, remote applications control or remote display and secure access over IP networks. With its standards and capabilities, WebRTC provides a new vision of real-time communications services that can raise these challenges. In this paper we present a WebRTC-based middleware solution for real-time multi-users remote collaboration. It allows a full desktop setup where everyone can see what other users are doing and where they position themselves in the shared workspace. In contrast to standard WebRTC's Peer-to-Peer architecture, our system supports a synchronous communication model through a star topology. It also improves network bandwidth efficiency by using hardware video compression when the GPU resource is available, though assuring a very low latency streaming. In this way, we can maintain awareness and sense of presence without changing the usual practices of the users in front of a desktop. Several use cases are provided and a comparison of advantages and drawbacks of this solution is also presented to guide users in applying this technology under real-life conditions.
In recent years, we have seen several different approaches dealing with multiview compression. First, we can find the H264/MVC extension which generates quite heavy bitstreams when used on n-views autostereoscopic medias and does not allow inter-view reconstruction. Another solution relies on the MVD (MultiView+Depth) scheme which keeps p views (n > p > 1) and their associated depth-maps. This method is not suitable for multiview compression since it does not exploit the redundancy between the p views, moreover occlusion areas cannot be accurately filled. In this paper, we present our method based on the LDV (Layered Depth Video) approach which keeps one reference view with its associated depth-map and the n − 1 residual ones required to fill occluded areas. We first perform a global per-pixel matching step (providing a good consistency between each view) in order to generate one unified-color RGB texture (where a unique color is devoted to all pixels corresponding to the same 3D-point, thus avoiding illumination artifacts) and a signed integer disparity texture. Next, we extract the non-redundant information and store it into two textures (a unified-color one and a disparity one) containing the reference and the n − 1 residual views. The RGB texture is compressed with a conventional DCT or DWT-based algorithm and the disparity texture with a lossless dictionary algorithm. Then, we will discuss about the signal deformations generated by our approach.
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