Point Cloud Buffer Management for Inter-Prediction Encoding
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Solution Overview
Problem
Current methods for processing point cloud data face challenges in efficiently transmitting and receiving large amounts of data, particularly in addressing latency and encoding/decoding complexity, as well as managing buffer storage for geometry-point cloud compression (G-PCC) bitstreams, especially when inter-prediction techniques are applied.
Innovation Solution
A method and device for transmitting and receiving point cloud data that involves encoding geometry and attribute information using inter-prediction or intra-prediction, deriving reference regions from reconstructed data stored in a buffer, and entropy-encoding residual information, while managing buffer storage efficiently by storing temporally close data and removing distant data to optimize encoding and decoding performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If inter-prediction is applied to encode point cloud data using reference regions, then encoding efficiency and compression ratio are improved, but encoding and decoding complexity increases
Solution Approach 1:
The patent divides the point cloud data into multiple reference regions (e.g., left region, right region, upper region, lower region) relative to the current region being encoded. Each reference region can be independently processed and stored in the buffer, allowing the system to manage complexity through segmentation while maintaining the benefits of inter-prediction across multiple regions.
Solution Approach 2:
The patent performs preliminary actions by pre-defining the buffer structure and pre-establishing the reference region derivation rules before actual encoding. The buffer is configured to store reconstructed geometry information in advance, and the derivation of reference regions from this pre-stored data simplifies the encoding process during execution.
2Measurement precision
If buffer storage manages reconstructed geometry information for inter-prediction, then prediction accuracy improves, but buffer management complexity increases
Solution Approach 1:
The patent applies local quality by storing only the necessary portions of reconstructed geometry information in the buffer based on the specific reference regions needed for prediction. Instead of managing the entire point cloud in the buffer, the system selectively stores and manages only the relevant reference region data, reducing buffer management complexity while maintaining prediction accuracy for the current region.
3Reliability
If point cloud data is transmitted with geometry and attribute information, then data completeness is maintained, but transmission time and bandwidth requirements increase
Solution Approach 1:
The patent extracts only the essential information needed for reconstruction by separating geometry information and attribute information into distinct encoding streams. The geometry encoder extracts positional data while the attribute encoder extracts properties like color or texture, allowing for efficient transmission of only necessary data while maintaining completeness through structured reconstruction at the receiving end.
Data Source
AI summary
A point cloud data transmission method according to embodiments may comprise the steps of: acquiring point cloud data; encoding geometry information including the positions of points of the point cloud data by applying inter-prediction or intra-prediction; encoding attribute information including attribute values of the points of the point cloud data on the basis of the geometry information by applying the inter-prediction or intra-prediction; and transmitting the encoded geometry information, the encoded attribute information, and signaling information.