Point Cloud Attribute Decoding With Adaptive RAHT Layer Modes
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Solution Overview
Problem
Current attribute encoding schemes for point cloud data, such as RAHT, do not fully consider the distribution of Alternating Current (AC) components in different RAHT layers, leading to low encoding efficiency.
Innovation Solution
Adaptive selection of inter prediction and intra prediction modes for attribute encoding, determining the target encoding mode based on syntax identifier information, and performing attribute decoding/encoding accordingly.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If a fixed attribute encoding mode (e.g., RAHT transform) is applied to the entire sequence, then the encoding process is simplified and consistent, but the encoding efficiency is reduced due to not considering the distribution of AC components in different RAHT layers
Solution Approach 1:
The patent applies dynamics by transitioning from a static, fixed encoding mode to a dynamic, adaptive encoding mode. The system dynamically selects between inter prediction mode and intra prediction mode for different RAHT layers based on the distribution characteristics of AC components. This allows the encoding process to adapt to the specific characteristics of each layer, improving encoding efficiency while maintaining reasonable complexity through automated mode selection based on layer-specific analysis.
2Productivity
If adaptive selection of inter prediction mode and intra prediction mode is implemented, then encoding efficiency is improved by considering AC component distribution, but the device complexity and processing overhead increase
Solution Approach 1:
The patent applies parameter changes by modifying the encoding parameters (prediction mode selection) based on the analysis of AC component distribution in different RAHT layers. The system changes the encoding approach by selecting different prediction modes (inter or intra) depending on the characteristics of each layer, allowing efficient adaptation to varying data characteristics without requiring complete redesign of the encoding architecture.
Solution Approach 2:
The patent implements feedback by analyzing the distribution characteristics of AC components in each RAHT layer and using this information to guide the selection of prediction modes. The system receives feedback from the layer analysis and adjusts the encoding strategy accordingly, creating a closed-loop system that continuously optimizes encoding efficiency based on actual data characteristics.
Data Source
AI summary
An encoding method, a decoding method, and a storage medium are provided. The decoding method includes that: when it is determined that a node in the current layer allows attribute prediction, a bitstream is parsed to determine first syntax identification information; when the first syntax identification information indicates that the current layer allows adaptive selection of an inter prediction mode and/or an intra prediction mode, a bitstream is parsed to determine a target decoding mode for the current layer; and according to the target decoding mode, attribute decoding is performed on the node in the current layer to determine an attribute reconstruction value of the node in the current layer.