Matrix Compression Using RLE and Differential Column Encoding
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
High-resolution spatial data representations, such as geographic maps, become computationally inefficient and require significant memory and bandwidth due to large data sizes, leading to processing and storage challenges.
Innovation Solution
The technique involves compressing data matrices using run length encoding (RLE) for rows and differential encoding for columns, resulting in a highly compact set of arrays, which can be further compressed using ZIP, thereby reducing memory and bandwidth requirements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If high-resolution spatial data is stored and processed in full resolution, then data accuracy and visualization quality are maintained, but memory usage and processing time increase significantly
Solution Approach 1:
The patent segments the spatial data matrix into rows and columns, applying run length encoding to rows and differential encoding to columns. This segmentation allows the data to be compressed into a set of arrays while preserving the ability to reconstruct the original high-resolution data when needed, thus maintaining accuracy while reducing memory usage.
Solution Approach 2:
The patent changes the representation parameters of spatial data by encoding repetitive patterns (run length) and differential changes (column differences). This parameter transformation reduces the quantity of data stored while maintaining the ability to recover the original high-resolution spatial information, resolving the contradiction between data accuracy and memory consumption.
2Loss of information
If high-resolution spatial data is transferred between systems, then data completeness is ensured, but network bandwidth consumption increases
Solution Approach 1:
By segmenting the data into encoded arrays that capture the essential spatial information through run length and differential encoding, the patent enables transmission of compressed data that maintains completeness. The segmented structure allows efficient bandwidth usage while preserving all necessary spatial data for reconstruction.
Solution Approach 2:
The patent applies parameter changes by transforming the data representation into encoded formats that reduce transmission size. The run length encoding and differential column encoding change the data parameters to a more compact form, ensuring data completeness is maintained while significantly reducing bandwidth consumption during transfer.
3Measurement precision
If computational operations are performed on full-resolution spatial data, then processing accuracy is maintained, but computational efficiency decreases
Solution Approach 1:
The patent segments the spatial data into encoded row and column arrays, enabling computational operations to be performed on the compressed representation. This segmentation maintains processing accuracy by preserving the spatial relationships in encoded form while dramatically improving computational efficiency through reduced data volume.
Solution Approach 2:
By changing the data representation parameters to encoded formats (run length for rows, differential for columns), the patent enables efficient computational operations on compressed data. The parameter transformation maintains the spatial information necessary for accurate processing while improving computational efficiency through the reduced data size.
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for compressing rows of data stored in a first matrix using run length encoding (RLE) to produce an RLE encoded matrix. Compressing columns of the RLE encoded matrix into a set of arrays by differentially encoding data count values of the RLE encoded matrix, wherein each array in the set of arrays represents a column of the RLE encoded matrix.