Vertex Mesh Motion Vector Prediction With Limited Neighbor Coding
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Solution Overview
Problem
Existing methods for compressing 3D point clouds and polygonal meshes in immersive multimedia content are inefficient, requiring specialized hardware and involve complex calculations for vertex motion vectors, leading to high computational overhead.
Innovation Solution
Improved vertex motion vector predictor coding techniques that determine vertex neighbors based on a set limit and map VMV predictors to identifiers, reducing the complexity of motion coding by around 30%.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If complex calculations for vertex motion vectors are performed to achieve accurate motion prediction, then prediction accuracy is improved, but computational overhead increases
Solution Approach 1:
The patent segments the motion vector prediction process into distinct stages: identifying candidate neighbors, selecting predictors from candidates, and calculating final VMV. This segmentation allows for optimized processing at each stage, reducing overall computational complexity while maintaining accuracy.
Solution Approach 2:
The patent performs preliminary identification of candidate vertex neighbors and their motion vectors before the actual VMV calculation. By pre-organizing candidate predictors and their associated motion vectors, the system reduces the computational burden during the main prediction phase.
2Productivity
If specialized hardware is used for compressing 3D point clouds and polygonal meshes, then compression efficiency is improved, but device complexity and cost increase
Solution Approach 1:
The patent enables general-purpose processors to perform VMV calculations by providing clear algorithms and data structures. The method allows the system to serve its own compression needs without requiring specialized hardware, as the computation can be performed using standard processing units with optimized software implementation.
Solution Approach 2:
The patent replaces specialized hardware mechanisms with software-based algorithms for vertex motion vector calculation. By substituting dedicated hardware circuits with programmable software routines, the system achieves comparable functionality using general-purpose processors, thereby reducing hardware complexity and cost.
3Measurement precision
If the number of vertex neighbors is increased to improve prediction accuracy, then motion vector prediction quality is improved, but processing time increases
Solution Approach 1:
The patent applies partial action by selecting a limited number of candidate vertex neighbors (typically 1-3 candidates) rather than considering all possible neighbors. This partial approach provides sufficient prediction accuracy while significantly reducing processing time compared to exhaustive neighbor evaluation.
Solution Approach 2:
The patent applies different processing strategies to different vertices based on their local characteristics. For each vertex, the system identifies and processes only the relevant candidate neighbors needed for accurate prediction, rather than uniformly processing all vertices with the same number of neighbors. This localized approach optimizes the balance between accuracy and processing time.
Data Source
AI summary
An apparatus includes a communication interface configured to receive a compressed video bitstream and a processor operably coupled to the communication interface. The processor is configured to determine, for a vertex in the compressed video bitstream, one or more vertex neighbors based on a signaled limit to a number of the one or more vertex neighbors. The processor is also configured to identify, based on a vertex motion vector (VMV) identifier signaled in the compressed video bitstream, a VMV predictor from among a plurality of VMV predictors to use for the vertex. The processor is also configured to reconstruct a mesh frame based on the determined one or more vertex neighbors and the identified VMV predictor.