3D Point Cloud Context-Based Arithmetic Encoding
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Solution Overview
Problem
Current three-dimensional data encoding and decoding methods face inefficiencies in coding processes, particularly in representing and transmitting large amounts of three-dimensional data, such as point cloud data, which necessitates improved compression techniques to enhance data accumulation and transmission efficiency.
Innovation Solution
The proposed solution involves determining reference positions for three-dimensional points, generating information items indicating the presence of points at these positions, selecting appropriate contexts for processing, and applying arithmetic encoding and decoding methods to optimize the encoding and decoding processes, thereby improving coding efficiency by utilizing context-specific information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If point cloud data is stored and transmitted without compression, then data accuracy is maintained, but data transmission efficiency and storage capacity deteriorate due to the massive amount of three-dimensional data
Solution Approach 1:
The patent segments the point cloud data into multiple regions or blocks, and processes each segment independently with context information from neighboring segments. This segmentation allows for efficient compression while maintaining overall data accuracy by enabling parallel processing and reducing the complexity of encoding the entire point cloud as a single unit.
Solution Approach 2:
The patent applies context-based arithmetic encoding where different context models are used for different regions or positions within the point cloud data. By adapting the encoding parameters locally based on spatial relationships and neighboring point information, the system achieves better compression efficiency while preserving the accuracy of individual point positions.
2Productivity
If context-based arithmetic encoding is applied to compress point cloud data, then data transmission efficiency is improved, but encoding complexity increases due to the need to select and process multiple contexts
Solution Approach 1:
The patent performs preliminary organization and sorting of point cloud data before applying context-based arithmetic encoding. By pre-processing the data to establish spatial relationships and determine encoding contexts in advance, the system reduces the real-time encoding complexity while maintaining the compression benefits of context-based methods.
Solution Approach 2:
The patent dynamically adjusts encoding parameters such as context selection and arithmetic encoding precision based on the local characteristics of the point cloud data. By changing parameters adaptively rather than using fixed complex models, the system achieves efficient compression with reduced overall encoding complexity.
Data Source
AI summary
A three-dimensional data decoding method includes: selecting, from among contexts, a first context for an encoded first information item to be processed; and arithmetic-decoding the encoded first information item to be processed, using the first context, to generate a first information item to be processed. Encoded first information items including the encoded first information item to be processed are included in a bitstream generated by encoding an information item about positions of three-dimensional points. The encoded first information items are generated by arithmetic-encoding first information items including the first information item to be processed. The first information items each correspond to a different one of reference positions and indicate whether a three-dimensional point corresponding to a reference position corresponding to the first information item is present.