End-to-end lossless compression of high precision depth maps guided by pseudo-residual

Facing the explosion of massive amount of high precision depth maps, we pro-pose a novel end-to-end lossless compression method for high precision depth maps. The whole process is comprised of two sub-processes, named pre-processing of depth maps and deep lossless compression of processed depth maps. The deep lossless compression network consists of two sub-networks, named lossy compression net-work and lossless compression network. We leverage the concept of pseudo-residual to guide the generation of distribution for residual and avoid introducing context models. Our end-to-end lossless compression network outperforms tested learned and non-learned codecs with at least 7% reduction on real world datasets (DIODE and SementicKITTI) and has low computational cost. The visualization of the whole operations is shown as Figure 1. More details and code are available at https://git.openi.org.cn/OpenCompression/DeepLosslessCompression.