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This work aims to enable efficient digital rights management for volumetric video by introducing attribute-based selective coordinate encryption for point clouds. The method encrypts only a subset of coordinates to reduce computation and latency while maintaining security. Selective encryption makes point cloud frames distorted enough to block meaningful unauthorized viewing while still allowing basic visibility. The framework allows variation in the amount and type of encrypted coordinates (X, Y, Z, or combinations). Visual degradation is measured using standard point cloud quality metrics. Results show that encrypting only X coordinates reduces encryption time by 37% and decryption time by 46% compared to full encryption. Encrypting X and Y reduces these times by 20% and 36% while still degrading visual quality. Attribute-Based Encryption allows protected content to be cached and distributed without re-encryption, which reduces computation and latency. The current evaluation covers individual frames. Future work will cover full volumetric video streams and analyze caching gains during streaming with Attribute-Based Encryption.
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Cylindrical Coordinates for Lidar Point Cloud Compression
We present an efficient voxelization method to encode the geometry and attributes of 3D point clouds obtained from autonomous vehicles. Due to the circular scanning trajectory of sensors, the geometry of LiDAR point clouds is inherently different from that of point clouds captured from RGBD cameras. Our method exploits these specific properties to representing points in cylindrical coordinates instead of conventional Cartesian coordinates. We demonstrate that Region Adaptive Hierarchical Transform (RAHT) can be extended to this setting, leading to attribute encoding based on a volumetric partition in cylindrical coordinates. Experimental results show that our proposed voxelization outperforms conventional approaches based on Cartesian coordinates for this type of data. We observe a significant improvement in attribute coding performance with 5-10% reduction in bitrate and octree representation with 35-45% reduction in bits.
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- Award ID(s):
- 1956190
- PAR ID:
- 10340129
- Date Published:
- Journal Name:
- 2021 IEEE International Conference on Image Processing (ICIP)
- Page Range / eLocation ID:
- 3083 to 3087
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/ICIP42928.2021.9506448
