Mesh Vertex Displacement Coding for Dynamic Mesh Compression
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Solution Overview
Problem
Existing mesh compression standards fail to efficiently handle dynamic meshes with time varying connectivity information and attribute maps, particularly under real-time constraints, leading to significant data requirements and inefficiencies in storage and transmission.
Innovation Solution
A method involving subdividing a mesh sequence into vertices, computing displacement vectors, and entropy coding based on these vectors, including techniques like zerotree signaling and level of detail (LOD) context to optimize compression.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If a constant quantization step size is applied for all instances and mesh parts, then the encoding process is simple, but the quantization error becomes large and mesh regions are not encoded with appropriate precision
Solution Approach 1:
The patent applies different quantization step sizes to different mesh regions based on their importance and characteristics. Face regions with more polygons receive finer quantization (smaller step sizes) while body regions receive coarser quantization (larger step sizes), optimizing both precision and compression efficiency.
Solution Approach 2:
The patent dynamically adjusts quantization parameters (step sizes) based on mesh region characteristics, polygon density, and visual importance. This allows the encoding system to adapt quantization precision to local requirements rather than using a uniform constant throughout the entire mesh.
2Device complexity
If a base mesh is simplified to reduce complexity, then the mesh can be encoded more efficiently, but the displacement requires less accuracy in bitdepth
Solution Approach 1:
The patent applies different bitdepth requirements to different mesh regions. Face regions with higher polygon density and visual importance require higher bitdepth for displacement encoding, while body regions can use lower bitdepth, optimizing the balance between compression efficiency and reconstruction accuracy.
3Measurement precision
If dynamic mesh sequences are encoded with full detail to maintain quality, then the visual quality is preserved, but the data volume becomes large and real-time transmission is not achievable
Solution Approach 1:
The patent encodes different mesh regions at different quality levels. Face regions with more polygons and higher visual importance are encoded with higher precision and more bits, while body regions are encoded with lower precision and fewer bits, significantly reducing overall data volume while preserving critical visual quality.
Solution Approach 2:
The patent dynamically adjusts encoding parameters (quantization step sizes, bitdepth) based on mesh region characteristics and temporal information, allowing the system to optimize the balance between compression ratio and visual quality for each region and time frame.
Data Source
AI summary
A method and apparatus comprising computer code configured to cause a processor or processors to obtain an input mesh corresponding to volumetric data of at least one three-dimensional (3D) visual content, derive a curve of a mesh sequence from the input mesh corresponding to volumetric data, the curve including a plurality of vertices of the input mesh of the mesh sequence, subdivide the derived curve of the mesh sequence by adding subdivided vertices, computing a displacement vector for each of the plurality of subdivided vertices; and entropy code the volumetric data based on the displacement vector from at least one of the subdivided vertices.