We present a compression scheme for multiview imagery that facilitates high scalablity and accessibility of the compressed content. Our scheme relies upon constructing at a single base view a disparity model for a group of views and then utilizing this base-anchored model to infer disparity at all views belonging to the group. We employ a hierarchical disparitycompensated inter-view transform where the corresponding analysis and synthesis filters are applied along the geometric flows defined by the base-anchored disparity model. The output of this inter-view transform along with the disparity information are subjected to spatial wavelet transforms and embedded blockbased coding. Rate-distortion results reveal superior performance to the x.265 anchor chosen by the JPEG Pleno standards activity for the coding of multiview imagery captured by high density camera arrays.

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For efficient compression of lightfields that involve many views, it has been found preferable to explicitly communicate disparity/depth information at only a small subset of the view locations. In this study, we focus solely on inter-view prediction, which is fundamental to multi-view imagery compression, and itself depends upon the synthesis of disparity at new view locations. Current HDCA standardization activities consider a framework known as WaSP, that hierarchically predicts views, independently synthesizing the required disparity maps at the reference views for each prediction step. A potentially better approach is to progressively construct a unified multi-layered base-model for consistent disparity synthesis across many views. This paper improves significantly upon an existing base-model approach, demonstrating superior performance to WaSP. More generally, the paper investigates the implications of texture warping and disparity synthesis methods.