Scalable Point Cloud Compression Using RAHT Transform Partitions
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Solution Overview
Problem
Current methods for compressing and decompressing point cloud data lack scalability, particularly in terms of spatial location, signal-to-noise ratio, and temporal resolution, making them impractical for real-time processing and diverse computational capabilities.
Innovation Solution
The use of a region-adaptive hierarchical transform (RAHT) to encode point cloud data, allowing for separation into partitions that can be decoded based on spatial, temporal, and quality requirements, enabling scalable compression and decompression while maintaining computational efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If graph transform and arithmetic coding are used for point cloud compression, then rate-distortion performance is improved, but computational complexity increases making real-time processing infeasible
Solution Approach 1:
The patent changes the transform parameters from complex graph transforms to simpler wavelet-like transforms that can be implemented with standard filtering operations. This parameter change maintains rate-distortion performance while reducing computational complexity to levels feasible for real-time processing on consumer hardware.
Solution Approach 2:
The patent replaces the complex arithmetic coding mechanism with simpler entropy coding methods that are more efficient on standard hardware. This substitution maintains compression effectiveness while enabling real-time processing on graphics processing units and other consumer devices.
2Productivity
If prior compression approaches are used, then computational efficiency is improved, but scalability to different spatial resolutions and quality levels is lost
Solution Approach 1:
The patent segments the compressed bitstream into multiple scalability layers, allowing decoders to selectively process different portions of the data. This segmentation enables the system to maintain computational efficiency for base layer processing while adding scalability for enhanced resolution and quality layers as needed.
Solution Approach 2:
The patent introduces a new dimension of scalability by organizing compression layers in a hierarchical structure that supports spatial, temporal, and quality scalability. This dimensional organization allows efficient processing at lower layers while enabling extended functionality at higher layers without reprocessing entire datasets.
3Ease of operation
If prior compression approaches are used, then simplicity of processing is improved, but rate-distortion performance deteriorates in certain scenarios
Solution Approach 1:
The patent applies local quality enhancement by enabling different processing qualities for different spatial regions and temporal layers. The system can maintain simple processing for base layers while applying enhanced compression techniques to specific regions or layers where higher rate-distortion performance is needed, rather than uniformly across the entire dataset.
Data Source
AI summary
Innovations in scalable compression and decompression of point cloud data are described. For example, after an encoder uses a transform such as a region-adaptive hierarchical transform (“RAHT”) on attributes of occupied points in point cloud data, the encoder separates transform coefficients into partitions. The partitions can be associated with different regions of a point cloud frame (spatial location scalability), different spatial resolutions of point cloud data (spatial resolution scalability), different reconstruction quality levels (SNR scalability), different point cloud frames organized in temporal layers (temporal resolution scalability), or different combinations of the preceding types of partitions. For decoding, a decoder can select all of the partitions or a subset of the partitions. The decoder decodes encoded data for the selected partitions, applying an inverse transform such as an inverse RAHT to transform coefficients for attributes of occupied points in point cloud data.