Real 3D video capturing for multiscopic rendering with controlled distortion

Prévoteau, Jessica; Chalençon-Piotin, Sylvia; Debons, Didier; Lucas, Laurent; Rémion, Yannick

doi:10.1117/12.838829

Cited by 4 publications

(1 citation statement)

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“…Next, we arrange each aggregated residual layer as part of a 3D image cube containing 8 slices (the cube is completed with blank layers) and the 3D-DCT is computed for each 8x8x8 block. The depth size of the 3D-cube was set to 8 for two main reasons: first, it allows us to exploit the 1D-DCT fast computational algorithm along the depth axis, then our compression algorithm has to support sequences produced by our specific multiview capture system (presented by Prévoteau et al 12 ).…”

Section: The Dct Transformmentioning

confidence: 99%

Multiview image compression based on LDV scheme

et al. 2011

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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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Section: The Dct Transformmentioning

confidence: 99%