Point Cloud Attribute Encoding via Adaptive Quantization Weight
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
The challenge in point cloud compression lies in improving the prediction effect of color information during the encoding process, particularly in handling large volumes of point cloud data efficiently.
Innovation Solution
An encoding method that processes attribute information of a point cloud to obtain a predicted residual value, quantizes it using a quantization weight and step, and encodes it to generate a bitstream, while the decoding method inversely quantizes the residual value to reconstruct the attribute information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If octree encoding is performed on location information and color information is predicted according to the location information, then the point cloud compression is achieved, but the prediction effect of color information needs improvement
Solution Approach 1:
The patent introduces quantization weight as a new parameter to modify the quantization step dynamically. By changing the quantization parameter based on point importance and prediction accuracy, the system achieves better color information prediction without fundamentally altering the octree encoding structure, thus resolving the contradiction between prediction accuracy and system complexity
Solution Approach 2:
The patent performs preliminary classification of points based on their importance and prediction accuracy before the main quantization process. By pre-identifying which points require higher precision and calculating their specific quantization weights in advance, the system improves prediction效果 while maintaining efficient encoding, addressing the contradiction between accuracy and complexity
2Measurement precision
If a fixed quantization step is used for encoding, then the encoding process is simple, but the prediction accuracy of attribute information is limited
Solution Approach 1:
The patent transforms the fixed quantization step into a dynamic parameter by introducing quantization weight that varies for different points. The quantization step becomes adaptive based on point importance and prediction accuracy, allowing higher precision where needed while maintaining encoding efficiency through automated parameter adjustment, thus resolving the contradiction between measurement precision and productivity
Solution Approach 2:
The patent applies different quantization weights to different points based on their local characteristics and importance. Rather than using a uniform quantization step, the system tailors the quantization precision to each point's specific needs, achieving high prediction accuracy for critical points while maintaining overall encoding efficiency, thereby resolving the contradiction between local precision and global efficiency
Data Source
AI summary
Provided are a point cloud encoding method and decoding method, and an encoder and a decoder. The encoding method includes: processing attribute information of a target point in a point cloud, so as to obtain a predicted residual of the attribute information of the target point; quantizing the predicted residual on the basis of a quantized weight of the target point and a quantized stride of the target point, so as to obtain a quantized residual of the attribute information of the target point, wherein the quantized weight of the target point is the weight used when weighting the quantized stride of the target point; and encoding the quantized residual, so as to obtain a code stream.