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

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End-To-End Depth Map Compression Framework Via Rgb-To-Depth Structure Priors Learning
Title:
End-To-End Depth Map Compression Framework Via Rgb-To-Depth Structure Priors Learning
Journal Title:
2022 IEEE International Conference on Image Processing (ICIP)
Keywords:
Publication Date:
18 October 2022
Citation:
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
Abstract:
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.
License type:
Publisher Copyright
Funding Info:
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
Description:
© 2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
ISBN:
978-1-6654-9621-6
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