RAHT Coefficient Quantization for Weight-Adaptive Point Cloud Coding
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Solution Overview
Problem
Existing point cloud video coding technologies face inefficiencies in quantization step size determination, particularly for region-adaptive hierarchical transform (RAHT) coefficients, which may not be optimized based on weight variations and uniform treatment of transform coefficients post-upsampled prediction.
Innovation Solution
Adaptive quantization methods are introduced, where the quantization step size for RAHT coefficients is determined by weight-based functions, allowing different step sizes for varying weights, and tailored quantization methods are applied to different attribute channels and regions, with 2×2×2 QP offset matrices used to optimize quantization based on octree layers, weights, and attribute channels.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If uniform quantization step size is used for all RAHT coefficients, then device complexity is reduced, but coding efficiency deteriorates
Solution Approach 1:
The patent applies different quantization step sizes to different RAHT coefficients based on their weights. Specifically, coefficients with higher weights use smaller quantization step sizes to preserve important information, while coefficients with lower weights use larger step sizes to reduce bitrate. This local differentiation resolves the contradiction by optimizing coding efficiency without requiring complex global optimization mechanisms.
Solution Approach 2:
The patent changes the quantization parameter (step size) based on the weight of each RAHT coefficient. The quantization step size is dynamically adjusted according to a predefined function of the coefficient weight, allowing the system to adapt the quantization aggressiveness to the importance of each coefficient, thereby improving coding efficiency while maintaining manageable complexity.
2Productivity
If weight-based adaptive quantization is applied to all RAHT coefficients, then coding efficiency is improved, but device complexity increases
Solution Approach 1:
The patent selectively applies weight-based adaptive quantization only to alternating current (AC) coefficients, while direct current (DC) coefficients are handled differently (inherited from parent nodes or quantized uniformly). This selective application reduces the complexity overhead compared to applying adaptive quantization to all coefficients, while still achieving significant coding efficiency improvements where they are most needed.
Solution Approach 2:
The patent segments the quantization process into different handling for DC and AC coefficients. DC coefficients are quantized uniformly or inherited, while AC coefficients receive weight-based adaptive quantization. This segmentation allows the system to apply complexity only where beneficial, resolving the contradiction between coding efficiency and device complexity.
3Productivity
If different quantization methods are used for different attribute channels, then coding efficiency is improved, but device complexity increases
Solution Approach 1:
The patent applies different quantization strategies to different attribute channels based on their characteristics. For example, geometry attributes may use one quantization approach while color attributes use another. This channel-specific optimization improves coding efficiency by matching quantization methods to attribute properties, while the complexity increase is managed through the standardized framework of weight-based step size determination.
Data Source
AI summary
A mechanism for processing video data is disclosed. The mechanism includes determining a quantization based on region-adaptive hierarchical transform (RAHT) weight. A conversion is performed between a visual media data and a bitstream based on the quantization.