Adaptive Update Weights for 3D Mesh Displacement Compression
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Solution Overview
Problem
The existing technologies for encoding and decoding 3D meshes face challenges in efficiently compressing and transmitting volumetric visual data, which results in large data sizes and high resource consumption, especially in applications like Augmented Reality (AR), Virtual Reality (VR), and Mixed Reality (MR).
Innovation Solution
The proposed solution involves a mesh coding/decoding system that uses a combination of techniques such as mesh subdivision, displacement encoding, and wavelet transforms to efficiently encode and decode 3D mesh sequences. This system applies adaptive update weights based on levels of detail (LODs) to optimize compression performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If traditional mesh coding/decoding methods are used, then the 3D mesh data can be encoded and decoded, but the data size is large and resource consumption is high
Solution Approach 1:
The patent segments the 3D mesh data into multiple levels of detail (LODs), where each LOD represents a different level of geometric complexity. This segmentation allows the system to process and compress only the necessary level of detail for each application scenario, significantly reducing the overall data size while maintaining visual quality. The lifting wavelet transform is then applied to each segmented LOD independently, enabling efficient compression at multiple scales.
Solution Approach 2:
The patent applies local quality by using adaptive update weights that are specific to each LOD level. Instead of applying a uniform compression approach to all mesh data, the system tailors the compression parameters and update weights to the local characteristics of each LOD. This allows for optimized compression performance at different levels of detail, where higher LODs may use different weighting strategies than lower LODs, thereby improving overall compression efficiency without sacrificing critical geometric information.
2Quantity of substance
If compression is applied to reduce data size, then storage and transmission efficiency improve, but visual quality may deteriorate
Solution Approach 1:
The patent employs dynamic adaptive update weights that can be adjusted based on the specific characteristics of the mesh data and the desired quality level. The update weights are not fixed but are adapted during the compression process to preserve important geometric features while discarding less significant details. This dynamic approach allows the system to maintain visual quality even as compression ratios increase, as the weights can be tuned to protect critical visual information.
Solution Approach 2:
The patent utilizes parameter changes by modifying the update weights in the lifting wavelet transform based on the LOD level and local mesh characteristics. By changing these parameters adaptively, the system can control the trade-off between compression ratio and visual quality. Higher quality requirements can be met by adjusting the parameters to retain more detail information, while lower quality requirements allow for more aggressive compression, providing flexible quality control throughout the compression process.
Data Source
AI summary
A decoder decodes, from a bitstream, transformed coefficients representing displacements of vertices of a three-dimensional mesh. The decoder selects, for a transformed coefficient of the transformed coefficients, a first transformed coefficient and a second transformed coefficient from the transformed coefficients and that are associated with the transformed coefficient. The first transformed coefficient is at a first level of detail (LOD) and the second transformed coefficient is at a second LOD, with the first and second LODs being lower than an LOD of the transformed coefficient. The first transformed coefficient is updated according to a first update weight based on the first LOD. The second transformed coefficient is updated according to a second update weight based on the second LOD. The decoder inverse transforms, based on the updated first transformed coefficient and the updated second transformed coefficient, the transformed coefficient to reconstruct the displacements.