Kavli Affiliate: Wei Gao
| First 5 Authors: Yuyang Wu, Wei Gao, , ,
| Summary:
As a fundamental data format representing spatial information, depth map is
widely used in signal processing and computer vision fields. Massive amount of
high precision depth maps are produced with the rapid development of equipment
like laser scanner or LiDAR. Therefore, it is urgent to explore a new
compression method with better compression ratio for high precision depth maps.
Utilizing the wide spread deep learning environment, we propose an end-to-end
learning-based 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
network 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
achieves competitive performance over engineered codecs and has low
computational cost.
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