Multidimensional Data Compression Using Position-Value Dimension Reduction
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Solution Overview
Problem
Existing data compression methods for three-dimensional and higher-dimensional data result in low compression ratios, leading to inefficient use of storage resources and communication bandwidth.
Innovation Solution
Perform dimension-reduced compression by using multidimensional data as position information and value information, combined with data form configurations, quantization, and projection modes to enhance compression efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of substance
If traditional compression methods are used on three-dimensional and higher-dimensional data, then the data can be compressed, but the compression ratio is low
Solution Approach 1:
The patent transforms P-dimensional data into Q-dimensional data where Q < P, effectively reducing the dimensionality of the data structure. This dimensional reduction allows the compression system to handle high-dimensional data (such as 3D point cloud data) by projecting it into lower-dimensional space, thereby achieving significantly improved compression ratios while maintaining essential data characteristics
2Loss of substance
If data is compressed to save storage resources, then storage efficiency improves, but communication efficiency may be affected
Solution Approach 1:
The patent changes key parameters including dimensionality (from P to Q), data representation format (using position-value pairs), and compression granularity. These parameter changes enable the system to achieve both reduced storage requirements and improved communication efficiency by transmitting only essential compressed data rather than full-dimensional data
3Productivity
If dimension-reduced compression is performed, then storage and communication efficiency improve, but data representation complexity increases
Solution Approach 1:
The patent segments P-dimensional data into position information (Q-dimensional coordinates) and value information (remaining P-Q dimensional attributes). This segmentation allows the complex high-dimensional data to be processed and transmitted as separate, more manageable components, reducing the overall complexity of data representation while maintaining compression efficiency
Data Source
AI summary
This application provides a data compression and transmission method and an apparatus. The method includes: A first communication apparatus performs compression processing on P-dimensional first data, to obtain Q-dimensional second data, where P is an integer greater than 1, and Q is less than P. The first communication apparatus sends the second data to a second communication apparatus, and may use Q-dimensional data in the first data as position information and remaining (P−Q)-dimensional data as value information, to obtain the second data based on the position information and the value information. In this way, efficient data compression is implemented.