3D Data Encoding Using Identification Information for Prediction Tree Boundaries
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Solution Overview
Problem
Current three-dimensional data encoding and decoding methods lack an effective mechanism for efficiently transmitting and receiving required information, particularly in applications involving point cloud data, and fail to support multiplexing and transmission of data using multiple codecs, leading to issues with data accumulation and format storage.
Innovation Solution
A three-dimensional data encoding method that generates a data unit including prediction trees and identification information, where the identification information indicates the presence of subsequent prediction trees, allowing for efficient decoding and identification of data unit boundaries, and a three-dimensional data decoding method that decodes prediction trees to calculate three-dimensional points based on this information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If point cloud data is compressed using conventional encoding methods, then data transmission efficiency is improved, but decoding effectiveness and boundary identification become difficult
Solution Approach 1:
An identification information item is introduced as an intermediary element between prediction trees in the bitstream. This identifier acts as a mediator that signals the presence or absence of subsequent prediction trees, enabling the decoder to effectively identify data unit boundaries without compromising compression efficiency
Solution Approach 2:
The point cloud data is segmented into multiple prediction trees, each representing a specific spatial region or attribute. By dividing the data into manageable prediction tree units with clear boundary identifiers, the system achieves both efficient compression and effective decoding boundary identification
2Reliability
If multiple prediction trees are included in a data unit, then data representation completeness is improved, but data unit complexity increases
Solution Approach 1:
The identification information item serves as a structural mediator that organizes multiple prediction trees within a data unit. It provides a clear hierarchical structure that indicates whether subsequent prediction trees exist, making the complex multi-tree data unit manageable and interpretable for decoders
Solution Approach 2:
The data unit structure is made dynamic through the identification information item, which adaptively indicates the presence or absence of subsequent prediction trees. This dynamic signaling allows the data unit to flexibly represent varying levels of data completeness without requiring fixed complex structures
3Measurement precision
If identification information is added to indicate subsequent prediction trees, then data unit boundary identification is improved, but bitstream volume increases
Solution Approach 1:
The identification information item is designed as a compact, minimal overhead element in the bitstream. It uses efficient encoding to convey boundary identification information with minimal bit consumption, acting as a disposable signaling element that provides precise boundary identification without significantly increasing overall bitstream volume
Data Source
AI summary
A three-dimensional data encoding method includes: obtaining three-dimensional points; generating a data unit including one or more prediction trees and one or more identification information items, using the three-dimensional points; and generating a bitstream including the data unit. The data unit includes one identification information item out of the one or more identification information items subsequent to one prediction tree out of the one or more prediction trees, and the one identification information item indicates whether there is a subsequent prediction tree subsequent to the one identification information item in the data unit.