3D Point Cloud Entropy Decoding With Prediction-Based Context Switching
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Solution Overview
Problem
Existing methods for encoding and decoding three-dimensional data are inefficient and result in excessive data handling, particularly due to inadequate context selection in intra prediction schemes.
Innovation Solution
A method and device that determine a numerical range based on whether intra or inter prediction is used, switching contexts between first and second contexts for entropy decoding or encoding, based on the absolute value of decoded information, to improve encoding efficiency and reduce data handling.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If a single context is used for entropy encoding/decoding regardless of prediction type, then device complexity is reduced, but encoding efficiency deteriorates
Solution Approach 1:
The patent segments the encoding/decoding process by creating separate contexts for intra-prediction and inter-prediction modes. The entropy encoder/decoder switches between a first context (for intra-prediction) and a second context (for inter-prediction) based on the prediction type, allowing optimized probability models for each mode while maintaining manageable complexity through clear segmentation.
Solution Approach 2:
The patent implements dynamic context selection where the entropy coding context is adaptively switched based on the prediction mode. The system dynamically adjusts the probability model by selecting different contexts according to whether intra-prediction or inter-prediction is used, enabling the encoding/decoding process to adapt to different data characteristics.
2Productivity
If context switching is implemented based on prediction type, then encoding efficiency is improved, but processing overhead increases
Solution Approach 1:
The patent applies preliminary action by determining the prediction type (intra or inter) before performing entropy encoding/decoding. This allows the system to pre-select the appropriate context before processing the actual data, avoiding runtime decisions and reducing processing overhead during the critical encoding/decoding path.
3Quantity of substance
If numerical range determination is performed for context switching, then data compression ratio is improved, but computational complexity increases
Solution Approach 1:
The patent applies local quality by determining numerical ranges specific to each prediction mode (intra vs. inter). Different numerical ranges are established for different contexts, allowing the system to optimize for local data characteristics of each prediction type while maintaining overall system efficiency through mode-specific parameter optimization.
Data Source
AI summary
A decoding method includes: determining a numerical range based on whether intra prediction or inter prediction is to be used to decode encoded first information of a current three-dimensional point; and switching a context between a first context and a second context to entropy-decode the encoded first information, based on whether an absolute value of decoded second information of the current three-dimensional point is included in the numerical range determined.