Static Mesh Entropy Coding with Shared Context for Normal Residuals
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Solution Overview
Problem
Existing video-based coding technologies for dynamic meshes, such as V-DMC, face increased complexity and storage requirements due to the use of different contexts for entropy encoding and decoding of normal vector attributes, leading to inefficient encoding and decoding processes.
Innovation Solution
Implementing a shared context scheme for entropy encoding and decoding of normal vector attributes, reducing the complexity and storage requirements by reusing contexts across different syntax elements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If different contexts are used for entropy encoding and decoding of normal vector attributes, then encoding precision is improved, but device complexity and storage requirements increase
Solution Approach 1:
The patent merges the context structures by making the fine prediction context a subset of the coarse prediction context. Specifically, the coarse context includes all bins from the fine context plus additional bins for sign and overflow information. This allows a single unified context to serve both fine and coarse prediction modes, reducing device complexity and storage requirements while maintaining encoding precision through context inheritance and reuse.
Solution Approach 2:
The unified context structure serves multiple functions: it handles both fine and coarse prediction modes, manages different bin types (sign, overflow, magnitude), and adapts to different prediction scenarios. This multi-functional context design eliminates the need for separate context structures, reducing complexity while preserving the precision benefits of differentiated encoding strategies.
2Measurement precision
If different contexts are used for entropy encoding and decoding of normal vector attributes, then encoding accuracy is improved, but storage requirements increase
Solution Approach 1:
The patent combines multiple context structures into a single unified context that stores probability estimates for all bin types. Instead of maintaining separate context arrays for fine and coarse predictions, the unified context inherits and shares probability estimates across different prediction modes, significantly reducing the storage footprint while maintaining encoding accuracy through context reuse and adaptation.
3Measurement precision
If fine prediction process is used with sufficient neighboring vertices, then prediction accuracy is improved, but computational complexity increases
Solution Approach 1:
The patent implements a dynamic context adaptation mechanism where the encoder automatically selects between fine and coarse prediction modes based on the availability of neighboring vertices. The context structure dynamically adjusts its behavior: when sufficient neighbors are available, fine prediction with higher accuracy is enabled; when neighbors are insufficient, coarse prediction is used. This dynamic adaptation optimizes the balance between prediction accuracy and computational complexity without requiring manual intervention.
Data Source
AI summary
A device is configured to decode a mesh from a bitstream that includes the encoded mesh data, wherein, as part of decoding the mesh, one or more processors of the device are configured to determine, based on encoded mesh data, a base mesh that includes a set of vertices; apply the entropy decoding to first, second, and third entropy-encoded data comprises using a shared non-bypass context for entropy decoding at least one bin of each of the first truncated unary (TU) data, the second TU data, and the third TU data, where the first, second, and third TU data are included in binarized representations of syntax elements representing first and second residual values of components of normal vectors of vertices and a second residual value of a component of a normal vector of a vertex.