End-To-End Depth Map Compression Framework Via Rgb-To-Depth Structure Priors Learning

End-To-End Depth Map Compression Framework Via Rgb-To-Depth Structure Priors Learning

2022 IEEE International Conference on Image Processing (ICIP)

10.1109/ICIP46576.2022.9898073

http://dx.doi.org/10.1109/icip46576.2022.9898073

Minghui Chen, Pingping Zhang, Zhuo Chen, Yun Zhang, Xu Wang, Sam Kwong

18 October 2022

Chen, M., Zhang, P., Chen, Z., Zhang, Y., Wang, X., & Kwong, S. (2022). End-To-End Depth Map Compression Framework Via Rgb-To-Depth Structure Priors Learning. 2022 IEEE International Conference on Image Processing (ICIP). https://doi.org/10.1109/icip46576.2022.9898073

In this paper, we propose a novel framework to exploit and utilize the shared information inner RGB-D data for efficient depth map compression. Two main codecs, designed based on the existing end-to-end image compression network, are adopted for RGB image compression and enhanced depth image compression with RGB-to-Depth structure prior, respectively. In particular, we propose a Structure Prior Fusion (SPF) module to extract the structure information from both RGB and depth codecs at multi-scale feature levels and fuse the cross-modal feature to generate more efficient structure priors for depth compression. Extensive experiments show that the proposed framework can achieve competitive rate-distortion performance as well as RGB-D task-specific performance at depth map compression compared with the direct compression scheme.

Publisher Copyright

This research / project is supported by the A*STAR - AME Young Individual Research Grants (YIRG)
Grant Reference no. : A2084c0176

This research / project is supported by the A*STAR - AI3 HTPO Seed Fund (AHSF)
Grant Reference no. : C211118005

This work was supported in part by the National Natural Science Foundation of China (Grant 61871270), in part by the Shenzhen Natural Science Foundation under Grants JCYJ20200109110410133 and 20200812110350001

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