Matrix Compression Using RLE and Differential Column Encoding
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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 time increases and bandwidth constraints.
Innovation Solution
The technique involves compressing data matrices using run length encoding (RLE) for rows and differential encoding for columns, resulting in a compact set of arrays, which can be further compressed using ZIP, achieving high compression ratios and reducing memory and bandwidth requirements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If high-resolution spatial data representations are used, then visualization quality is improved, but memory usage and processing time increase significantly
Solution Approach 1:
The patent segments spatial data into discrete cells organized in a grid structure, where each cell represents a specific geographic region. This segmentation allows the large continuous spatial dataset to be divided into manageable units that can be processed and stored efficiently, resolving the contradiction between maintaining high resolution and reducing data size.
Solution Approach 2:
The patent transforms the two-dimensional spatial data into a one-dimensional compressed representation by encoding consecutive cells with the same data value as a single entry with a count. This dimensionality change reduces the data size while preserving the ability to reconstruct the original high-resolution spatial representation when needed.
2Measurement precision
If high-resolution spatial data is stored, then data accuracy is maintained, but memory resources are consumed excessively
Solution Approach 1:
The patent merges consecutive cells that contain identical data values into a single compressed entry representing multiple cells. This merging reduces memory usage by eliminating redundant storage of repeated values while maintaining the ability to recover the original accurate spatial data representation through decompression.
Solution Approach 2:
The patent changes the storage parameters from storing individual cell values to storing compressed representations that include data values and associated counts. This parameter change allows the system to maintain spatial data accuracy while significantly reducing the memory volume required to store the same information.
3Loss of information
If large spatial data matrices are transmitted, then complete data is delivered, but network bandwidth is consumed excessively
Solution Approach 1:
The patent applies compression encoding to spatial data matrices before transmission, preliminarily reducing the data size while preserving all essential information. This preliminary action ensures that complete data is delivered with higher fidelity while consuming less network bandwidth, as the compressed representation maintains the ability to reconstruct the original data.
4Measurement precision
If uncompressed spatial data is processed, then processing accuracy is maintained, but computational efficiency decreases
Solution Approach 1:
The patent implements a dynamic processing approach where spatial data can be stored and transmitted in compressed form, then decompressed on-demand for processing. This dynamic system allows the data to exist in different states (compressed/uncompressed) depending on the operational requirements, maintaining processing accuracy when needed while improving overall productivity through efficient storage and transmission.
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.