Haar-Based Point Cloud Coding for Faster 3D Attribute Compression
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Solution Overview
Problem
Point clouds, which are used for 3D representations of objects and environments, contain large amounts of data that are costly and time-consuming to store and transmit, necessitating efficient compression techniques.
Innovation Solution
The method employs Haar-based attribute coding, utilizing sorting methods like Hilbert sorting and improved quantization parameters, along with entropy coding, to compress point clouds effectively.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If point cloud data is stored and transmitted without compression, then data quality is preserved, but storage cost and transmission time increase significantly
Solution Approach 1:
The point cloud data is divided into multiple octants using octree structure, allowing independent processing and compression of different spatial regions. This segmentation enables parallel compression operations and reduces the overall transmission time while maintaining data quality through selective precision encoding of different octants.
Solution Approach 2:
The patent transforms 3D point cloud data into 2D attribute maps through projection techniques. By converting spatial data into planar representations, the patent reduces data dimensionality and enables more efficient compression algorithms to be applied, significantly reducing data volume while preserving essential geometric and attribute information.
2Productivity
If compression techniques are applied to point cloud data, then transmission speed improves, but reconstruction quality may deteriorate
Solution Approach 1:
The patent applies different compression strategies to different regions of the point cloud based on their importance and characteristics. Critical regions with high geometric detail or significant attribute variations are encoded with higher precision, while less important regions use more aggressive compression. This local quality approach maintains overall reconstruction quality while improving compression efficiency.
Solution Approach 2:
The patent encodes only the necessary amount of detail required for acceptable reconstruction quality rather than preserving all original data. By selectively encoding attributes and geometric information based on perceptual importance and application requirements, the patent achieves high compression ratios while maintaining sufficient reconstruction quality for most applications.
3Device complexity
If traditional compression methods are used, then implementation is simple, but compression ratio and speed are insufficient
Solution Approach 1:
The patent employs entropy coding techniques that automatically adapt to the statistical properties of the point cloud data without requiring manual parameter tuning. The coding scheme self-adjusts based on the local data characteristics, eliminating the need for complex pre-processing or parameter optimization while achieving high compression efficiency and speed.
Data Source
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AI summary
In a method of point cloud attribute decoding in a point cloud decoder, a sorting method of a set of points of a point is determined. A quantization parameter for quantizing transform coefficients of a Haar-based attribute coding of the point cloud is determined based on first signaling information in a coded bitstream of the point cloud. The Haar-based attribute coding is configured to perform point cloud compression on the set of points of the point cloud based on a Haar transform. An entropy decoding method for decoding the quantized transform coefficients is determined according to the quantization parameter that is determined based on the first signaling information. The quantized transform coefficients are further determined based on the entropy decoding method. Attributes of the point cloud are reconstructed based on the determined sorting method and the determined quantized transform coefficients.