Volumetric Mesh Encoding with LoD-Aware Coefficient Packing
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Traditional image coding methods fail to distinguish samples in a specified level of detail, leading to inefficient memory allocation even in partial reconstruction scenarios.
Innovation Solution
The method involves obtaining a volumetric mesh, performing mesh segmentation and decimation, calculating mesh displacements, converting to quantized coefficients, and rearranging them in a two-dimensional image using a specific scanning pattern and packing order.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional image coding methods are used to map three-dimensional displacement coefficients to two-dimensional image, then the encoding process is simple, but it is impossible to clearly distinguish samples belonging to specified levels of detail, resulting in inefficient memory allocation
Solution Approach 1:
The patent applies segmentation by dividing the volumetric mesh into multiple levels of detail (LoD), where each LoD represents a specific level of geometric precision. This segmentation allows the system to distinguish and process samples at different detail levels separately, enabling selective memory allocation based on the required precision for different regions of the image.
Solution Approach 2:
The patent transforms the three-dimensional displacement coefficients into a two-dimensional image representation through a structured mapping process. By organizing the 3D coefficient data into 2D arrays with specific scanning patterns (e.g., raster scan, Morton order), the system enables efficient memory layout and processing while preserving the ability to distinguish different levels of detail through the hierarchical LoD structure.
2Reliability
If maximum memory is allocated for partial reconstruction scenario, then reconstruction quality is maintained, but memory usage is inefficient
Solution Approach 1:
The patent implements local quality by allowing different regions of the image to be reconstructed at different levels of detail. Regions requiring high precision (such as areas with important features or edges) can be reconstructed using higher LoD levels with more memory resources, while less critical regions can use lower LoD levels with reduced memory allocation. This localized approach maintains overall reconstruction quality while optimizing total memory consumption.
Solution Approach 2:
The patent applies partial action by enabling selective reconstruction of only the necessary portions of the image at full detail levels. Instead of allocating maximum memory for the entire image, the system identifies and reconstructs only the critical regions at their appropriate LoD levels, using memory resources proportionally to the actual reconstruction needs rather than uniformly across all pixels.
3Measurement precision
If volumetric mesh is processed with mesh segmentation and decimation, then levels of detail can be distinguished, but processing time increases
Solution Approach 1:
The patent applies preliminary action by pre-computing and storing the volumetric mesh in a hierarchical structure with multiple levels of detail before the actual encoding or reconstruction process. The mesh segmentation and decimation operations are performed in advance to create the LoD pyramid, allowing the system to quickly select and process only the necessary detail levels during runtime without performing computationally intensive mesh operations on-the-fly.
Data Source
AI summary
An encoding method is provided and includes: obtaining a volumetric mesh; performing mesh segmentation of the volumetric mesh to generate multiple segments of mesh content; performing mesh decimation of a segment of mesh content to generate a base mesh; performing mesh subdivision of the base mesh to generate multiple subdivided base meshes; calculating multiple mesh displacements between the multiple subdivided base meshes and an original volumetric mesh surface to generate multiple transformed displacement coefficients; converting the multiple transformed displacement coefficients to multiple quantized transformed displacement coefficients; scanning the multiple quantized transformed displacement coefficients along a three-dimensional space scanning pattern within each level-of-detail to form three one-dimensional arrays; and re-arranging the multiple quantized transformed displacement coefficients in the three one-dimensional arrays to a two-dimensional image according to the each level-of-detail and a packing order indicated by a specific flag.