Learning-Based Point Cloud Coding with Boundary-Aware Quantization
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing point cloud compression technologies face challenges in efficiently encoding and decoding large-scale point cloud data, particularly in real-time applications, leading to suboptimal performance and resource utilization.
Innovation Solution
A learning-based approach is employed to determine quantized values and boundary values for point cloud samples, utilizing artificial intelligence models and probability bitstreams to enhance encoding and decoding processes, thereby optimizing the compression and representation of point cloud data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If learning-based processes are used to determine quantized values for point cloud samples, then the accuracy and efficiency of compression is improved, but the computational complexity and resource requirements increase
Solution Approach 1:
The patent applies parameter changes by dynamically adjusting quantization parameters based on learned distributions and statistical properties of point cloud data. The system modifies quantization step sizes, boundary values, and sampling rates according to the specific characteristics of each point cloud sample, enabling efficient compression without requiring excessive computational resources for all samples uniformly.
Solution Approach 2:
The patent implements local quality by applying different compression strategies to different regions and samples within the point cloud. Instead of uniform compression, the system identifies important samples (e.g., those with high geometric detail or semantic significance) and applies stronger compression only where necessary, while using lighter compression for less critical regions, thereby reducing overall computational complexity.
2Quantity of substance
If quantization parameters are adjusted to improve compression ratio, then data size is reduced, but reconstruction quality and precision deteriorate
Solution Approach 1:
The patent applies dynamics by making quantization parameters adaptive rather than fixed. The system dynamically adjusts quantization step sizes and boundary values based on the statistical properties of each point cloud sample, allowing finer quantization in regions requiring high precision and coarser quantization where precision can be sacrificed for compression efficiency, thus balancing data size reduction with reconstruction quality.
Solution Approach 2:
The patent implements feedback mechanisms where the system continuously monitors reconstruction quality metrics and adjusts quantization parameters accordingly. The learned models provide feedback about the optimal quantization settings for each sample type, enabling the system to automatically tune parameters to maintain acceptable reconstruction quality while maximizing compression ratio.
3Speed
If real-time processing is implemented for point cloud compression, then response time is reduced, but computational resources and processing power requirements increase
Solution Approach 1:
The patent applies segmentation by dividing the point cloud into manageable segments or groups of samples that can be processed independently. This allows the system to apply compression algorithms to smaller subsets of data, reducing the computational burden on any single processing unit while maintaining real-time performance. The segmented approach enables parallel processing and distributes computational load more effectively.
Solution Approach 2:
The patent extracts and processes only the most critical information from each point cloud sample rather than treating all samples uniformly. By identifying and retaining only the essential geometric and semantic features while discarding redundant information, the system reduces the amount of data requiring processing, thereby lowering computational power requirements while maintaining real-time processing capability.
Data Source
AI summary
Some embodiments of a method may include: determining a first number by running a learning-based process, wherein the first number is associated with a current sample; obtaining a quantization parameter; determining a quantized value based on at least the quantization parameter for the first number; obtaining a sample set; responsive to determining that the current sample is not in the sample set, outputting the quantized value; and responsive to determining that the current sample is in the sample set, performing several steps comprising: determining a boundary value based on at least the quantization parameter and the first number; determining a second number based on the boundary value; and outputting the second number.