Multiview Geometry Quantization Using Edge Feature Atlases
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Solution Overview
Problem
Multiview images suffer from perceptible visual artifacts due to quantization errors when rendered at intermediate viewpoints, particularly when higher frequency components are quantized using a higher quantization step.
Innovation Solution
Generate an edge feature atlas to indicate edge or discontinuity samples, using a smaller quantization step for samples at edges, reducing quantization errors and visual artifacts in reconstructed scenes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If a higher quantization step is used to quantize higher frequency components of a geometry atlas, then compression efficiency is improved, but visual artifacts become perceptible in rendered scenes at intermediate viewpoints
Solution Approach 1:
The patent applies local quality by differentiating quantization treatment based on spatial location. Samples identified as being at edges or discontinuities (through edge feature detection) receive a smaller quantization step, while non-edge samples use a larger quantization step. This localized differentiation preserves visual quality at critical boundaries while maintaining compression efficiency in other regions, directly resolving the contradiction between compression and artifact reduction.
Solution Approach 2:
The patent implements preliminary action by generating an edge feature atlas before the quantization process. This edge map is created in advance to identify which samples require special treatment. By pre-processing the geometry atlas to mark edge locations, the system prepares the necessary information before quantization occurs, enabling selective quantization that prevents visual artifacts while maintaining overall compression efficiency.
2Object-affected harmful factors
If a smaller quantization step is used for all samples, then visual artifacts are reduced, but compression efficiency decreases
Solution Approach 1:
Instead of uniformly applying a small quantization step to all samples, the patent uses local quality by applying different quantization steps to different regions. Edge samples use a smaller quantization step to prevent artifacts, while non-edge samples use a larger quantization step to maintain compression efficiency. This spatially-varying approach resolves the contradiction by optimizing for visual quality only where necessary.
Solution Approach 2:
The patent changes the quantization parameter dynamically based on sample location. The quantization step size is adjusted according to edge detection results: a first (smaller) quantization step is applied to edge samples, while a second (larger) quantization step is applied to non-edge samples. This parameter adaptation allows the system to achieve both artifact reduction and compression efficiency.
3Object-affected harmful factors
If quantization errors are reduced at edge samples, then visual artifacts such as flying points and object bloating are minimized, but processing complexity increases
Solution Approach 1:
The patent reduces processing complexity during the main quantization stage by performing edge detection in advance. The edge feature atlas is generated before quantization, so that during the actual encoding process, the system only needs to reference pre-computed edge information rather than performing complex edge detection and analysis during quantization. This preliminary preparation significantly reduces the computational burden during the main processing phase.
Solution Approach 2:
The edge feature atlas serves as an intermediary data structure that simplifies the quantization process. Instead of directly analyzing each sample's edge characteristics during quantization, the system uses this intermediate edge map to quickly determine which samples require special treatment. This intermediary representation reduces the complexity of the main quantization algorithm by providing pre-processed guidance information.
Data Source
AI summary
Multiview images may comprise attribute frames and geometry frames. Samples of a geometry frames may comprise depth information corresponding to collocated samples of the attribute frames. Additional edge feature frames may be generated, for the multiview images, with samples of the edge feature frame indicating whether collocated samples of the geometry frames are at edges and/or discontinuities. Information from the edge feature frame may be used to correct quantization errors that may be associated with samples, of the geometry frames, that are located at edges and discontinuities.