Instance-Based Mesh Coding with Adaptive Quantization
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Solution Overview
Problem
Existing video coding technologies face challenges in efficiently compressing complex meshes with varying polygon densities and importance across different regions, leading to significant quantization errors due to uniform quantization steps.
Innovation Solution
The method involves partitioning input meshes into submeshes based on similarity, determining bounding box ratios, and applying different codecs to each submesh, allowing for tailored encoding and prediction strategies.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If a constant quantization step size is applied for all instances, objects, or parts in mesh, then the encoding process is simple, but large quantization errors occur because mesh regions are not equally important and polygon densities vary significantly
Solution Approach 1:
The mesh is divided into multiple submeshes based on geometric characteristics, object types, or importance levels. Each submesh can then be encoded with different quantization parameters, allowing important regions to maintain higher precision while less important regions use coarser quantization, thus resolving the contradiction between encoding simplicity and quantization accuracy.
Solution Approach 2:
Different quantization step sizes are assigned to different submeshes based on their local characteristics such as curvature, importance, or polygon density. This allows critical mesh regions to preserve high geometric fidelity while non-critical regions use simpler encoding, achieving both accuracy where needed and simplicity where acceptable.
2Manufacturing precision
If the mesh is segmented into several parts based on characteristics, then quantization accuracy can be improved, but the device complexity and encoding process become more complex
Solution Approach 1:
The mesh segmentation is performed based on clear geometric or semantic criteria that can be automatically detected, such as object boundaries, curvature thresholds, or importance masks. This automated segmentation reduces the manual complexity while enabling differentiated quantization strategies for each submesh, improving accuracy without proportionally increasing process complexity.
Solution Approach 2:
Different quantization parameters (step sizes, precision levels) are dynamically assigned to different submeshes based on their characteristics. This parameter differentiation allows the system to achieve high overall accuracy by optimizing parameters locally rather than using a single complex encoding process for the entire mesh.
3Manufacturing precision
If high quality encoding is applied to all mesh regions, then the overall mesh quality is improved, but the data size and processing requirements increase significantly
Solution Approach 1:
High-quality encoding with fine quantization is applied only to submeshes identified as important (e.g., facial regions in human models, critical structural areas), while less important regions use coarser quantization. This selective quality approach maintains perceptual mesh quality in critical areas while significantly reducing overall data size by applying lower precision where it is not needed.
Solution Approach 2:
The mesh is segmented into importance-based groups or regions, allowing differentiated encoding strategies. Critical regions receive high-quality encoding to preserve essential geometric details, while non-critical regions use compressed representations, achieving an optimal balance between overall mesh quality and data size efficiency.
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, partitioning the input mesh into a plurality of submeshes, each of the submeshes comprising a respective one of instances of objects, determine a similarity between at least two of the instance of objects, grouping the submeshes based on the determined similarity, and code the volumetric data based on grouping the submeshes.