Dynamic Mesh Motion Coding Using Graph Fourier Transforms
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Solution Overview
Problem
Existing methods for encoding motion data in dynamic meshes require significant computational resources and do not effectively utilize spatiotemporal correlations, leading to inefficient compression and high bitrates.
Innovation Solution
Represent motion vectors in a Graph Fourier Transform (GFT) domain, either explicitly based on intra-frame mesh connectivity or implicitly based on inter-frame connectivity, to discover and utilize signal correlations, enabling progressive reconstruction with reduced bitrates and low computational complexity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If motion data is encoded directly in spatial domain, then encoding process is simple, but bitrate is high and compression efficiency is low
Solution Approach 1:
The patent transforms motion data from spatial domain to spectral domain using Graph Fourier Transform, changing the representation parameters from vertex positions to frequency coefficients. This transformation reveals hidden correlations in the spectral domain that enable more efficient compression while maintaining reconstruction accuracy.
Solution Approach 2:
The patent replaces traditional spatial-domain motion encoding with a spectral-domain approach using GFT. This substitution allows the system to exploit frequency-domain correlations that are not apparent in the spatial domain, achieving better compression ratios without proportionally increasing complexity.
2Manufacturing precision
If high quality mesh representation is used, then rendering quality is high, but data amount is large
Solution Approach 1:
The patent extracts and encodes only the most significant spectral coefficients rather than all motion data. By identifying and retaining only the dominant frequency components that contribute most to mesh quality, the system achieves high reconstruction quality with substantially reduced data amounts.
Solution Approach 2:
The patent applies different encoding precision to different frequency bands, using higher precision for low-frequency coefficients that dominate mesh structure and lower precision for high-frequency coefficients. This local quality approach maintains overall mesh quality while reducing total data requirements.
3Productivity
If traditional motion encoding is used, then computational resources required are high, but compression efficiency is low
Solution Approach 1:
The patent computes only the necessary spectral coefficients required for adequate mesh reconstruction rather than processing all motion data in full detail. By performing partial GFT computation focused on dominant frequency components, the system achieves good compression efficiency with reduced computational resource consumption.
Data Source
AI summary
Apparatuses and methods are disclosed for encoding and for decoding mesh data. Disclosed encoding techniques include receiving a mesh sequence, including geometry data of vertices of meshes in the sequence and coding motion data into a bitstream of coded mesh data. The motion data coding comprises transforming, based on a Graph Fourier Transform (GFT), the geometry data to obtain GFT coefficients representative of the motion data, and then coding the GFT coefficients into the bitstream. Disclosed decoding techniques include receiving a bitstream of coded mesh data, including coded motion data and decoding the motion data from the bitstream. The decoding of the motion data comprises decoding GFT coefficients representative of the motion data, and then inverse transforming, based on the GFT, the decoded GFT coefficients to obtain decoded geometry data of vertices of meshes in the sequence.