Mesh Attribute Prediction Using Adaptive Predictor Ordering
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing mesh compression techniques are inefficient in selecting predictors for polygon meshes, leading to suboptimal prediction accuracy and increased bit usage for signaling predictor indices.
Innovation Solution
Adaptive selection and ordering of predictors based on mesh type and face type characteristics, such as triangle or quad meshes, to improve prediction accuracy and reduce bit usage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a fixed predictor selection method is used for mesh compression, then the device complexity is reduced, but the prediction accuracy deteriorates
Solution Approach 1:
The patent implements dynamic predictor selection by adapting the predictor subset and ordering based on mesh type (triangle, quad, or mixed) and face type characteristics. Instead of using a fixed predictor list, the system dynamically selects from different candidate predictors depending on the local mesh geometry, thereby improving prediction accuracy while managing complexity through structured adaptation rules.
Solution Approach 2:
The patent applies local quality by selecting different predictors for different regions of the mesh based on face type and mesh type characteristics. Each face or vertex can utilize a locally optimized predictor from the candidate set, allowing the prediction accuracy to be tailored to local geometric properties rather than applying a uniform approach across the entire mesh.
2Measurement precision
If more bits are used to signal predictor indices, then the predictor selection precision is improved, but the compression efficiency deteriorates
Solution Approach 1:
The patent changes the parameter of predictor indexing by using context-adaptive binary arithmetic coding (CABAC) with context models that are selected based on mesh type and face type. This allows the encoder to adaptively allocate bit precision for predictor indices based on the local geometry, using fewer bits for predictable regions and more bits where higher precision is needed, thereby optimizing compression efficiency while maintaining necessary indexing precision.
3Measurement precision
If adaptive predictor selection based on mesh type and face type is implemented, then the prediction accuracy is improved, but the signaling overhead increases
Solution Approach 1:
The patent implements universality by designing a unified adaptive predictor selection framework that handles multiple mesh types (triangle, quad, mixed) and various face types through a single coherent mechanism. The same CABAC-based signaling approach and predictor candidate sets are used across different mesh types, with adaptation achieved through context selection rather than separate encoding paths, thereby reducing overall signaling overhead while maintaining prediction accuracy.
Data Source
AI summary
A method includes receiving a bitstream including coded information of the one or more meshes. One of (i) a first subset of predictors from a list of candidate predictors and (ii) a first order of predictors in the first subset of predictors used to predict a first attribute associated with a first vertex incident onto a first face in a first mesh of the one or more meshes is determined based on one of (i) a first mesh type of the first mesh and (ii) a first face type of the first face. The first mesh type indicates the most frequent face degree of the first mesh. The first face type indicates a face degree of the first face. The first attribute is predicted based on the determined one of (i) the first subset of predictors and (ii) the first order of predictors in the first subset of predictors.