Point Cloud Data Transmission Using Segmented Quantization
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Solution Overview
Problem
Current methods for processing point cloud data face challenges in efficiently handling large amounts of data, leading to latency and encoding/decoding complexity, particularly in applications like virtual reality, augmented reality, and self-driving services.
Innovation Solution
A method and device for encoding and decoding point cloud data, including geometry and attribute information, to transmit efficiently through bitstreams, utilizing techniques such as geometry-based point cloud compression and video-based point cloud compression, enabling high-quality point cloud services.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If point cloud data is transmitted with high quality, then service quality improves, but data transmission complexity increases
Solution Approach 1:
The point cloud data is divided into multiple blocks or partitions, allowing independent encoding and transmission of different segments. This segmentation reduces the overall complexity by breaking down large data structures into manageable units that can be processed and transmitted more efficiently.
Solution Approach 2:
The patent employs multiple quantization parameters (QP) with different precision levels for different blocks of point cloud data. By varying the precision parameter across different data segments, the system achieves high overall quality while managing transmission complexity through selective precision allocation.
2Measurement precision
If point cloud data is processed with high precision, then data accuracy improves, but processing time increases
Solution Approach 1:
Different quantization parameters are applied to different blocks of point cloud data, allowing high precision processing only where necessary while using lower precision for other regions. This parameter variation maintains data accuracy for critical areas while reducing overall processing time.
Solution Approach 2:
The patent applies high-precision processing selectively to certain blocks rather than uniformly across all data. This partial application of high precision maintains necessary accuracy while avoiding the time cost of processing every data point at maximum precision.
3Manufacturing precision
If quantization parameter precision is increased, then encoding accuracy improves, but encoding complexity increases
Solution Approach 1:
The patent uses multiple quantization parameters with different precision levels for different blocks. This approach maintains high encoding accuracy where needed while reducing encoding complexity through selective precision application, avoiding the need to process all data at maximum precision.
Solution Approach 2:
By dividing the point cloud data into multiple blocks and applying different quantization parameters to each, the system manages encoding complexity through segmentation while maintaining overall encoding accuracy through the use of high-precision parameters for critical blocks.
Data Source
AI summary
A point cloud data transmission method according to embodiments comprises the steps of: encoding point cloud data including geometry data and attribute data; and transmitting a bitstream including the point cloud data. A point cloud data reception method according to embodiments comprises the steps of: receiving a bitstream including point cloud data including geometry data and attribute data; and decoding the point cloud data.