3D Point Attribute Encoding With Common-Value Bitstreams
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Solution Overview
Problem
Existing three-dimensional data encoding and decoding methods lack efficient coding techniques, leading to excessive data amounts and inefficient transmission and processing of three-dimensional data.
Innovation Solution
The proposed method involves encoding geometry and attribute information of three-dimensional points, generating bitstreams with encoded values and common values, and decoding these bitstreams to improve coding efficiency by reducing redundant data transmission.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If all attribute values of three-dimensional points are encoded individually, then complete information is preserved, but data volume increases excessively
Solution Approach 1:
The patent merges identical attribute values by encoding them once as a common value with an identifier, rather than encoding each occurrence separately. This combining approach preserves complete attribute information while significantly reducing data volume by eliminating redundant encodings of identical values.
Solution Approach 2:
The patent changes the encoding parameter from individual value encoding to dual-mode encoding (individual encoded values or common value with identifier). This parameter change allows the system to adaptively select the most efficient encoding method based on attribute value distribution, optimizing the balance between information completeness and data volume.
2Quantity of substance
If common values are used for all three-dimensional points, then data volume is minimized, but information completeness is lost
Solution Approach 1:
The patent merges identical attribute values into a single common value representation with an identifier, preserving the information that multiple points share the same attribute value. This approach minimizes data volume while maintaining information completeness by allowing reconstruction of individual point attributes from the common value and identifier.
Solution Approach 2:
The patent implements a parameter change in the encoding strategy, allowing dynamic selection between encoding individual values and using common values with identifiers. This enables the system to optimize for data volume reduction while preserving the ability to retrieve complete attribute information when needed.
3Loss of information
If individual attribute values are encoded for each point, then information completeness is maintained, but processing complexity increases
Solution Approach 1:
The patent merges the encoding process into a dual-mode system that combines individual value encoding with common value encoding. This merging approach maintains information completeness while reducing processing complexity by avoiding redundant encoding operations for identical attribute values across multiple points.
Solution Approach 2:
The patent introduces a parameter change in the encoding methodology, enabling adaptive selection between individual and common value encoding modes. This parameter change simplifies processing by allowing the system to choose the most efficient encoding approach based on the specific characteristics of the attribute data being processed.
4Productivity
If common value encoding is used, then processing efficiency is improved, but adaptability to different data patterns is reduced
Solution Approach 1:
The patent merges individual value encoding and common value encoding into a unified dual-mode system. This merging provides both high coding efficiency through common value encoding and adaptability through the ability to switch to individual value encoding when attribute values vary significantly, thus resolving the contradiction between efficiency and adaptability.
Solution Approach 2:
The patent implements a dynamic encoding system that can adaptively switch between individual value encoding and common value encoding modes based on the characteristics of the attribute data. This dynamic approach maintains high coding efficiency for data with repeated values while preserving adaptability to handle diverse data patterns, including cases where individual encoding is more appropriate.
Data Source
AI summary
A three-dimensional data encoding method includes: encoding geometry information of three-dimensional points; generating a first bitstream including first information indicating encoded values when a value of an attribute of each of the three-dimensional points is encoded to generate the encoded values; and generating a second bitstream including second information indicating a common value of the attribute which is common between the three-dimensional points when the value of the attribute of each of the three-dimensional points is not encoded.