Alphabet-Partition Transform Coefficient Coding for Sparse Point Clouds
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Solution Overview
Problem
Current graph-based point cloud compression technologies face inefficiencies in compression efficiency when there are not enough neighboring attribute samples available for prediction, particularly in lossy compression for real-time communications and dynamic mapping applications.
Innovation Solution
The method involves decomposing transform coefficients into set-index values and symbol-index values, partitioning them into sets, entropy-coding the set-index values, and bypass-coding the symbol-index values to improve compression efficiency by leveraging frequency-sorted look-up tables and caches.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If distance-based weighted average prediction is used for attribute coding, then prediction accuracy is improved when neighboring points are available, but compression efficiency deteriorates when not enough neighboring attribute samples are available
Solution Approach 1:
The patent introduces transform coefficients as an intermediary representation that captures attribute variations in a transformed domain. By applying transforms (e.g., PCA, DCT) to groups of attribute values, the method converts spatial correlation into spectral domain coefficients, enabling efficient compression even when direct neighboring samples are insufficient for prediction.
Solution Approach 2:
The patent changes the representation parameters of attribute data by applying linear transforms to convert original attribute values into transform coefficients. This parameter transformation allows the data to be represented in a form where energy is concentrated in fewer coefficients, improving compression efficiency regardless of the availability of neighboring samples for prediction.
2Ease of manufacture
If traditional attribute coding methods are used, then implementation simplicity is maintained, but compression efficiency is insufficient for real-time communications and dynamic mapping applications
Solution Approach 1:
The patent segments attribute data into groups that can be processed independently through transforms. By dividing the attribute coding process into transformation, coefficient encoding, and reconstruction stages, the method achieves higher compression efficiency while maintaining manageable implementation complexity through modular processing.
Solution Approach 2:
The patent replaces traditional mechanical prediction-based attribute coding with a transform-based mathematical approach. Instead of relying on spatial neighborhood relationships, the method uses linear algebra transforms to capture attribute correlations, substituting geometric reasoning with algebraic operations that are more efficient for compression.
3Productivity
If more complex coding schemes are applied to improve compression, then compression efficiency is improved, but computational complexity and memory requirements increase
Solution Approach 1:
The patent applies transforms selectively to groups of attribute values rather than processing all data uniformly. By transforming only necessary groups and using entropy coding for transform coefficients, the method achieves adequate compression efficiency without requiring excessive computational resources, balancing compression performance with implementation feasibility.
Data Source
AI summary
A method, apparatus, and computer-readable medium for point cloud coefficient coding are provided. Transform coefficients associated with point cloud data are decomposed into set-index values and symbol-index values, the symbol index-value specifying location of the transform coefficient within a set. The decomposed transform coefficients are partitioned into one or more sets based on the set-index values and the symbol-index values. The set-index values of the partitioned transform coefficients are entropy-coded, and the symbol-index values of the partitioned transform coefficients are bypass-coded. The point cloud data is compressed based on the entropy-coded symbol-index values and the bypass-coded set-index values.