Point Cloud Attribute Decoding With Selective RAHT
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
The existing RAHT method for attribute transform encoding and decoding in point cloud compression is complex and inefficient, leading to redundancy and low coding efficiency due to the sequential transformation and prediction of nodes at each level.
Innovation Solution
A method to determine the first and second node numbers to decide whether to perform RAHT on the nodes of a current level, using reconstructed attribute values based on these node numbers to optimize the encoding and decoding process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If RAHT transform is performed on all nodes at each level sequentially, then attribute transformation can be applied, but the complexity of encoding and decoding increases and redundancy cannot be effectively removed
Solution Approach 1:
The patent applies local quality by selectively performing RAHT transform only on specific nodes (leaf nodes) at certain levels of the octree structure, rather than uniformly applying it to all nodes. The decision to apply RAHT is based on local characteristics such as whether a node is a leaf node and its position in the hierarchy, thereby reducing overall complexity while maintaining transformation quality where needed.
Solution Approach 2:
The patent segments the octree structure into different levels and node types (leaf nodes vs. internal nodes), and applies different processing strategies to each segment. By dividing the transformation process into level-based segments and only applying RAHT to specific segments (leaf nodes at certain levels), the system reduces computational complexity while preserving essential attribute transformation capabilities.
2Productivity
If sequential transformation and prediction of nodes is performed, then attribute coding can be completed, but redundancy removal is ineffective and coding efficiency is low
Solution Approach 1:
The patent performs preliminary actions by pre-determining which nodes are suitable for RAHT transform based on their structural characteristics (leaf node status, level in hierarchy) before actually performing the transformation. This preliminary selection allows the system to focus computational resources on nodes where RAHT will be most effective, thereby removing redundancy more efficiently and improving overall coding performance.
Solution Approach 2:
The patent changes the parameter of transformation application from a uniform approach to a selective approach based on node characteristics. By adjusting the transformation parameter (applying RAHT only when node type and level conditions are met), the system optimizes redundancy removal while maintaining coding efficiency, avoiding the pitfalls of applying transformations universally.
Data Source
AI summary
Embodiments of the present application provide an encoding/decoding method and a non-transitory computer-readable storage medium, including: an encoder/decoder determining a first node quantity of nodes of a current layer and a second node quantity of child nodes corresponding to the nodes of the current layer, wherein the first node quantity and the second node quantity are used for determining whether to perform RAHT on the nodes of the current layer; and, according to the first node quantity and the second node quantity, determining attribute reconstruction values of the child nodes corresponding to the nodes of the current layer.