Scalable depth maps with R-D optimized embedding

Recent work has highlighted the importance of incorporating geometry information into the compression of depth maps. In prior approaches however the geometry information is not resolution scalable nor amenable to embedded coding. In this paper we propose a novel compression strategy for depth maps that incorporates geometry information while achieving the goals of scalability and embedded representation. Our scheme involves two separate image pyramid structures, one for breakpoints and other for sub-band samples produced by a breakpoint-adaptive transform. Breakpoints capture geometric attributes and are amenable to scalable coding. We develop an R-D optimization framework for the breakpoint data. We also use a variation of the EBCOT scheme to produce embedded bit-streams for both the breakpoint and sub-band data, allowing them to be independently and incrementally sequenced based on R-D considerations