Point Cloud Coder Mapping for Fewer Coders and Better Compression
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Solution Overview
Problem
Current data compression methods for point clouds are inefficient due to the large number of binary coders required, leading to impractical implementation and sub-optimal compression performance, especially for smaller point clouds, as they struggle to accurately evolve the probabilities of less frequently used coders.
Innovation Solution
The use of a coder mapping look-up-table that updates on-the-fly based on the content being coded, reducing the number of binary coders by selecting one based on a dependency state and adapting the mapping after each symbol is coded, allowing for context adaptation and reducing the number of coders while maintaining precision.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a large number of binary coders are used to accurately represent all possible dependency states, then coding precision is improved, but device complexity and implementation difficulty increase significantly
Solution Approach 1:
The patent segments the large set of dependency states into multiple smaller groups, where each group is associated with a subset of binary coders. Instead of using one large coder for all states, the system divides the coding task into multiple smaller coding tasks, each handling a specific dependency state group. This segmentation reduces the complexity of individual coders while maintaining overall coding precision through the collective coverage of multiple coder subsets.
Solution Approach 2:
The patent introduces a new dimension to the coding system by organizing coders into multiple subsets and associating them with different dependency state groups. This dimensional organization allows the system to manage complexity by adding a structural layer (subsets and groups) rather than simply increasing the number of coders linearly. The multidimensional organization enables efficient mapping between dependency states and coder subsets.
2Device complexity
If the number of binary coders is reduced to simplify implementation, then device complexity is reduced, but coding precision deteriorates due to insufficient coder coverage
Solution Approach 1:
The patent merges multiple subsets of binary coders into a unified coding system where each subset handles specific dependency states. By combining these subsets strategically, the system achieves comprehensive coverage of all dependency states using fewer total coders than a traditional single-large-coder approach. The merging is done in a way that preserves precision by ensuring each dependency state maps to an appropriate coder within its designated subset.
Solution Approach 2:
Each subset of binary coders is designed to be multi-functional, capable of handling multiple dependency states within its group. This universality allows a single coder subset to serve multiple purposes, reducing the overall number of specialized coders needed. The multi-functional design maintains precision by adapting to different dependency states within each subset's capability range.
3Productivity
If context adaptation is implemented to improve compression performance, then compression efficiency is improved, but the number of coders and system complexity increases
Solution Approach 1:
The patent implements dynamic context adaptation by allowing the association between dependency states and coder subsets to change based on the coding context. The system can dynamically select which subset to use for a given dependency state based on statistical properties of the data being coded. This dynamic behavior improves compression efficiency by adapting to local data characteristics while managing complexity through the pre-organized subset structure.
Solution Approach 2:
The system incorporates feedback mechanisms that use information from previously coded data to inform subset selection for subsequent dependency states. By analyzing coding results and statistical patterns, the system provides feedback that guides the selection of appropriate coder subsets, improving compression efficiency through context-aware adaptation. The feedback loop operates within the framework of the organized subsets, preventing exponential complexity growth.
Data Source
AI summary
Methods and systems for encoding and decoding data, such as point cloud data. The methods may include using a coder map to map a range of discrete dependency states to a smaller set of binary coders each having an associated coding probability. The selection of one of the discrete dependency states may be based on a contextual or situational factors, which may include a prediction process, for a particular symbol, such as an occupancy bit. The coder map is updated after each symbol is coded to possibly alter to which binary coder the selected discrete dependency state maps.