Point Cloud Data Compression Using Prediction Trees
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Solution Overview
Problem
Existing methods for processing point cloud data are inefficient in handling large amounts of data, leading to issues with latency, encoding/decoding complexity, and compression efficiency.
Innovation Solution
A method and apparatus for efficiently transmitting and receiving point cloud data using a prediction-based coding approach, which involves encoding geometry data, encoding attribute data based on geometry data, and transmitting the encoded data along with signaling information. This method includes dividing geometry data into compression units, generating predictive trees, and compressing data using prediction based on these trees.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If point cloud data is transmitted without compression, then data quality is preserved, but transmission efficiency and processing speed deteriorate due to large data volume
Solution Approach 1:
The point cloud data is divided into multiple compression units (CU), each of which can be independently processed and transmitted. This segmentation allows for efficient parallel processing while maintaining overall data quality, resolving the contradiction between preserving data quality and improving transmission efficiency.
Solution Approach 2:
Prediction trees are constructed in advance during the encoding process, enabling the decoder to reconstruct point cloud data more efficiently. This preliminary action reduces the computational complexity at the decoding stage, thereby improving transmission efficiency without sacrificing data quality.
2Productivity
If compression is applied to reduce data volume, then transmission efficiency improves, but encoding/decoding complexity increases
Solution Approach 1:
By dividing the point cloud into multiple compression units with independent prediction trees, the encoding and decoding processes can be parallelized. This segmentation reduces the overall computational complexity while maintaining compression efficiency, thus improving transmission efficiency without excessive complexity increase.
Solution Approach 2:
The prediction trees are constructed to utilize local spatial correlations within each compression unit, allowing the data to encode/decode itself with minimal external processing. This self-service approach reduces the burden on the encoding/decoding system while maintaining compression effectiveness.
3Productivity
If prediction-based coding is applied to compress point cloud data, then compression efficiency improves, but latency increases due to additional processing
Solution Approach 1:
The point cloud data is segmented into multiple compression units that can be processed independently and in parallel. This segmentation reduces the processing time for each unit while maintaining overall compression efficiency, thereby reducing latency without sacrificing compression performance.
Solution Approach 2:
The prediction process focuses on the most significant spatial correlations within each compression unit rather than attempting to model all possible relationships. This partial action approach achieves sufficient compression efficiency while minimizing processing time and latency.
4Productivity
If point cloud data is divided into multiple compression units, then processing parallelism improves, but system complexity increases due to multiple prediction trees
Solution Approach 1:
The division into multiple compression units with independent prediction trees enables parallel processing, improving productivity. The modular structure of each compression unit makes the system manageable despite the increased number of components, balancing parallelism gains with acceptable system complexity.
Data Source
AI summary
A point cloud data transmission method according to embodiments comprises the steps of: encoding geometry data of point cloud data; encoding attribute data of the point cloud data on the basis of the geometry data; and transmitting the encoded geometry data, the encoded attribute data, and signaling data. The step of encoding the geometry data may comprise the steps of: dividing the geometry data into a plurality of compression units; generating, for each compression unit, a prediction tree on the basis of the geometry data within each compression unit; and compressing the geometry data within the compression unit by performing prediction on the basis of the prediction tree.