Octree Occupancy Coding with Dynamic OBUF Context Selection
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing geometry-based point cloud compression techniques suffer from sub-optimal context selection accuracy in occupancy coding, particularly when using inter prediction, leading to inefficiencies in data compression and potential loss of detail in reconstructed point clouds.
Innovation Solution
The proposed technique employs a dynamic optimal binary coder with update on the fly (OBUF) to determine the occupancy of a reference child node in a reference frame, using this information to select a more accurate context for entropy coding of the current node, thereby improving coding efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If occupancy coding uses traditional context selection without inter prediction, then the coding process is simpler, but context selection accuracy is insufficient leading to poor compression efficiency
Solution Approach 1:
The patent applies preliminary action by determining the occupancy of a reference child node in a reference frame before coding the current child node. This pre-computed reference occupancy information is then used to select the appropriate context for entropy coding, improving context selection accuracy without adding complexity during the actual coding process.
Solution Approach 2:
The patent uses an intermediary approach by introducing a reference frame as a mediator between the current frame and the occupancy coding process. The reference child node occupancy serves as intermediate information that bridges the gap between available data and the coding decision, enabling more accurate context selection.
2Productivity
If occupancy coding does not use inter prediction, then device complexity is lower, but data compression efficiency is reduced
Solution Approach 1:
The patent performs preliminary determination of reference child node occupancy before the actual coding operation. This pre-computed information enables more efficient entropy coding by providing accurate context models in advance, thereby improving compression efficiency without significantly increasing operational complexity.
Solution Approach 2:
The patent implements dynamic context selection where the context model is adapted based on the determined reference occupancy. This dynamic adjustment allows the coding system to optimize compression efficiency for each specific case by selecting the most appropriate context, balancing the trade-off between complexity and efficiency.
3Reliability
If traditional occupancy coding is used, then the coding process is faster, but loss of detail in reconstructed point clouds increases
Solution Approach 1:
The patent determines reference occupancy information in advance, which enables more accurate context selection during coding. This preliminary determination allows the system to achieve better reconstruction quality through improved context models while maintaining coding speed by avoiding complex computations during the actual encoding process.
Data Source
AI summary
A G-PCC coder may determine an occupancy of a reference child node in a reference node, wherein the reference node is in a reference frame of point cloud data used for inter prediction of a current node in a current frame of the point cloud data. The G-PCC coder may further determine a context for decoding a current occupancy bit of a current child node of the current node based on the occupancy of the reference child node, and arithmetic decode the current occupancy bit using the context.