Point Cloud Compression Using Dimension-Specific Reference Frames
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional point cloud encoding techniques only permit the use of a single reference frame for inter prediction, which can lead to suboptimal compression efficiency for point clouds with varying types of motion.
Innovation Solution
Employing different reference frames for different dimensions of a point cloud's coordinate system, such as using a zero-compensated reference frame for certain dimensions and a global motion compensated reference frame for others, to generate inter predictors that better match the motion characteristics of the point cloud data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If a single reference frame is used for inter prediction, then the encoding process is simple, but compression efficiency is suboptimal for varying motion types
Solution Approach 1:
The patent divides the point cloud encoding process into different dimensional segments (azimuth dimension and distance dimension) and applies different reference frames to each segment. The azimuth dimension uses a zero-compensated reference frame while the distance dimension uses a global motion compensated reference frame, allowing each dimension to be optimized independently for its specific motion characteristics.
Solution Approach 2:
The patent applies different reference frame qualities to different dimensions based on their specific motion characteristics. Instead of using a uniform reference frame for all dimensions, the system selects the most appropriate reference frame (zero-compensated or global motion compensated) for each dimension, improving local optimization of compression efficiency.
2Productivity
If different reference frames are used for different dimensions, then compression efficiency improves, but the system complexity increases
Solution Approach 1:
The system segments the prediction process by dimension and applies different reference frames accordingly. This segmentation allows complex motion compensation to be applied only where needed (distance dimension) while keeping other dimensions (azimuth dimension) simpler, thus managing overall system complexity through targeted complexity application.
Solution Approach 2:
The patent implements a dynamic reference frame selection mechanism where the encoder adapts the reference frame type based on the motion characteristics detected in each dimension. This dynamic adaptation allows the system to optimize compression efficiency while managing complexity by only using complex compensation when the data exhibits corresponding motion patterns.
Data Source
AI summary
A method comprises: for each of a plurality of dimensions: identifying a reference position for the dimension, the reference position for the dimension being a position in a reference frame for the respective dimension, and the reference frame for the respective dimension and a reference frame for at least one other dimension in the plurality of dimensions being different reference frames in a plurality of reference frames; identifying an inter predictor for the respective dimension, wherein a predictor has a coordinate value in the respective dimension corresponding to a coordinate value in the respective dimension of the inter predictor for the respective dimension; and encoding or decoding the current point based on the predictor.