A fundamental element of stereoscopic and/or autostereoscopic image production is the geometrical analysis of the shooting and viewing conditions in order to obtain a qualitative 3D perception experience. This paper firstly compares the perceived depth with the shot scene depth from the viewing and shooting geometries for a couple of shooting and rendering devices. This yields a depth distortion model whose parameters are expressed from the geometrical characteristics of shooting and rendering devices. Secondly, these expressions are inverted in order to design convenient shooting layouts yielding chosen distortions on specific rendering devices. Thirdly, this design scheme provides three shooting technologies (3D computer graphics software, photo rail, and camera box system) producing qualitative 3D content for various kinds of scenes (real or virtual, still or animated), complying with any prechosen distortion when rendered on any specific multiscopic technology or device formerly specified.
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.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.