View-Dependent Quantization for Geometric Data Compression
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Solution Overview
Problem
Existing quantization techniques distribute quantized locations uniformly, leading to consistent quantization error throughout a scene, resulting in perceivable artifacts close to the viewpoint due to uniform coarse spacing of quantization points.
Innovation Solution
Implementing view-dependent quantization values that are less coarse close to the viewpoint and more coarse with increasing distance, using transformations like z'= 1/(1+z) to adjust distances and minimize error, allowing for adaptive quantization based on proximity to the viewpoint.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If uniform quantization distribution is used, then data representation consistency is maintained, but quantization error becomes perceivable as artifacts near the viewpoint
Solution Approach 1:
The patent applies local quality by making quantization precision view-dependent rather than uniform. Quantization values are adjusted based on distance from the viewpoint, with finer precision allocated to regions closer to the viewpoint and coarser precision to distant regions. This resolves the contradiction by maintaining high precision where it matters most (near the viewpoint) while accepting coarser precision in less critical areas, thereby eliminating perceivable artifacts without uniformly increasing data complexity throughout the entire scene.
2Object-affected harmful factors
If view-dependent quantization is implemented, then artifacts near viewpoint are reduced, but quantization complexity increases
Solution Approach 1:
The patent implements parameter changes by transforming the quantization approach from uniform to view-dependent based on distance parameters. A transformation function (such as z' = 1/(1+z)) is applied to adjust quantization values according to their distance from the viewpoint. This resolves the contradiction by systematically varying quantization parameters based on spatial relationships, reducing artifacts through adaptive precision while maintaining manageable complexity through mathematical transformation rather than complex algorithms.
3Ease of manufacture
If uniform quantization spacing is used, then implementation simplicity is maintained, but memory usage increases due to coarse spacing throughout the scene
Solution Approach 1:
The patent applies local quality by implementing non-uniform quantization spacing that adapts to viewing distance. Regions closer to the viewpoint use finer spacing to preserve detail, while distant regions use coarser spacing to reduce data volume. This resolves the contradiction by optimizing memory usage through spatially-varying precision - storing more data only where it is visually important and less data where it is less critical, thereby reducing overall memory requirements while maintaining implementation feasibility through systematic distance-based categorization.
Data Source
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AI summary
An encoder includes a processor and a memory. The memory includes code as instructions that cause the processor to receive geometric data and a viewpoint (POV) associated with the geometric data, and quantize the geometric data based on the viewpoint using a plurality of view-dependent quantization values (115) each having a quantized distance based on a proximity to the viewpoint.