Base-Anchored Model for Highly Scalable and Accessible Compression of Multiview Imagery

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Base-Anchored Model for Highly Scalable and Accessible Compression of Multiview Imagery

By: 
Dominic Rüefenacht; Aous Thabit Naman; Reji Mathew; David Taub

We present a compression scheme for multiview imagery that facilitates high scalability 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 disparity-compensated 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 is subjected to spatial wavelet transforms and embedded block-based 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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