Adaptive Linear Lifting Transform for Dynamic Mesh Compression
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Solution Overview
Problem
Existing mesh compression standards struggle to efficiently compress dynamic meshes with time-varying connectivity information and attribute maps, which are essential for immersive 3D content across various platforms.
Innovation Solution
The proposed solution involves an adaptive linear lifting transform for mesh compression, which processes mesh data by determining prediction values for vertices based on their values and distances to neighboring vertices, enabling efficient encoding and decoding of mesh frames.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If existing mesh compression standards are used, then mesh data can be compressed, but the compression efficiency is insufficient for dynamic meshes with time-varying connectivity information and attribute maps
Solution Approach 1:
The patent applies dynamics by making the mesh compression adaptive to time-varying connectivity information and attribute maps. The compression method dynamically adjusts to changes in mesh structure across different time points, allowing efficient compression of dynamic meshes while maintaining fidelity. This is achieved through temporal prediction that exploits redundancy between consecutive mesh frames with varying connectivity.
Solution Approach 2:
The patent changes parameters by introducing adaptive prediction modes that switch between different compression strategies based on the characteristics of the mesh data. The method adjusts compression parameters dynamically according to the temporal and spatial variations in mesh connectivity and attribute maps, optimizing the balance between compression ratio and reconstruction quality.
2Measurement precision
If more data is transmitted to maintain mesh quality, then mesh reconstruction accuracy improves, but transmission bandwidth requirement increases
Solution Approach 1:
The patent implements feedback through temporal prediction mechanisms that use previously decoded mesh frames to predict current frame characteristics. The decoder uses feedback from reconstructed meshes to improve prediction accuracy, reducing the amount of data needed while maintaining reconstruction precision. This feedback loop allows the system to adapt to mesh dynamics efficiently.
Solution Approach 2:
The patent applies preliminary action by performing prediction and compression operations using information from previous frames before the actual decoding of the current frame. This preliminary processing of temporal redundancy reduces the data volume that needs to be transmitted, while the prediction models are pre-configured to maintain reconstruction accuracy.
3Productivity
If complex compression algorithms are used, then compression ratio improves, but computational complexity increases
Solution Approach 1:
The patent segments the mesh compression process into distinct stages: temporal prediction, residual computation, and encoding. By dividing the complex compression task into manageable segments, the method achieves high compression ratios while keeping each individual stage computationally efficient. The segmentation allows parallel processing and optimization of each stage independently.
Solution Approach 2:
The patent applies partial action by selectively applying complex prediction algorithms only to regions of the mesh that require high precision, while using simpler methods for other regions. This selective approach maintains overall compression efficiency without requiring full computational complexity across the entire mesh, reducing the average computational burden.
Data Source
AI summary
In a method of mesh decoding, a bitstream that includes prediction information of a plurality of vertices in a mesh frame is received. A prediction value of a current vertex of the plurality of vertices is determined based on (i) a value of the current vertex received in the bitstream and (ii) each distance between the current vertex and one or more neighboring vertices of the current vertex in the mesh frame. The current vertex is reconstructed based on the prediction value of the current vertex.