Adaptive Coder Mapping for Point Cloud Compression Precision
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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 sub-optimal compression performance, especially for smaller datasets, as they struggle to accurately encode the geometry of sparsely populated point clouds.
Innovation Solution
The implementation of a coder mapping system that uses a look-up table to select binary coders based on dependency states and updates the mapping after each symbol is coded, reducing the number of binary coders while maintaining precision, by employing a finite set of binary coders with associated probabilities and adapting on-the-fly to the characteristics of the data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a large number of binary coders are used to accurately encode point cloud data, then encoding precision is improved, but device complexity and computational overhead increase
Solution Approach 1:
The patent combines multiple binary coders into a unified arithmetic coder that processes multiple symbols simultaneously. Instead of using separate binary coders for each symbol, the system merges them into a single arithmetic coding mechanism that handles multiple occupancy bits in parallel, reducing the number of coders while maintaining encoding precision.
Solution Approach 2:
The arithmetic coder is designed to serve multiple functions by processing different symbols and dependency states within a single coding framework. The same arithmetic coder structure is used to encode multiple occupancy bits with different probability distributions, eliminating the need for multiple specialized binary coders.
2Productivity
If context-based coding with predictions is used for point clouds, then compression performance is improved, but the complexity of context selection and management increases
Solution Approach 1:
The patent changes the parameter representation by using probability values directly as context identifiers in the arithmetic coder. Instead of managing separate context structures for different prediction outcomes, the system maps prediction probabilities directly to coding parameters, simplifying context selection while maintaining compression performance.
Solution Approach 2:
The arithmetic coder acts as an intermediary that bridges prediction and coding operations. Rather than requiring separate context selection logic for each prediction outcome, the arithmetic coder intermediate layer automatically adapts to different prediction scenarios through its probability-based parameter adjustment mechanism.
3Measurement precision
If adaptive coding methods are used to handle varying point cloud densities, then encoding accuracy is improved, but processing time and computational resources increase
Solution Approach 1:
The patent performs preliminary probability estimation for multiple symbols before actual coding occurs. By pre-calculating probability values for occupancy bits based on spatial and temporal predictions, the system prepares coding parameters in advance, reducing the computational burden during the actual encoding process and decreasing processing time.
Solution Approach 2:
The arithmetic coder maintains continuous adaptation to data statistics by updating probability models throughout the encoding process. This continuous adaptation allows the system to maintain high encoding accuracy for varying point cloud densities without requiring repeated recalibration, thereby reducing overall processing time compared to discrete adaptive methods.
Data Source
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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.