Gridded Data Compression with Variable Blocks to Limit Error Growth
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing data compression techniques face challenges in minimizing errors during compression and decompression, especially when dealing with large data sets transmitted over low bandwidth links like satellites, where efficient compression methods are needed to balance computational effort and tolerated loss.
Innovation Solution
The implementation of variable block size compression, efficient storage of null values, and methods to eliminate error growth in time series data, using techniques like Discrete Cosine Transform (DCT) and quantization, along with dynamic block subdivision and error tolerance thresholds, to optimize compression while maintaining minimal loss.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If data is compressed using traditional methods, then transmission efficiency is improved, but error growth occurs during decompression of time series data
Solution Approach 1:
The patent segments time series data into fixed-size blocks before compression. Each block is independently compressed and decompressed, preventing error propagation across the entire data set. This segmentation allows controlled error containment while maintaining overall data integrity for scientific analysis.
Solution Approach 2:
The patent performs preliminary compression and decompression testing on data blocks before final transmission. By pre-processing blocks to establish baseline error characteristics and applying appropriate compression parameters in advance, the system minimizes error growth during actual decompression operations.
2Quantity of substance
If compression ratio is increased to reduce data size, then transmission bandwidth is optimized, but decompression error increases
Solution Approach 1:
The patent dynamically adjusts compression parameters such as block size and compression level based on data characteristics and required precision. By changing these parameters, the system optimizes the balance between compression ratio and decompression accuracy, ensuring scientific data maintains sufficient precision while reducing data size for efficient transmission.
3Device complexity
If fixed block size compression is used, then processing is simplified, but error distribution becomes uneven across different data regions
Solution Approach 1:
The patent implements dynamic block sizing where block boundaries and compression parameters are adjusted based on local data characteristics. Regions with high variability use smaller blocks to maintain precision, while homogeneous regions use larger blocks for better compression. This dynamic adaptation ensures uniform error distribution across different data regions while managing processing complexity.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach reduces errors in data compression and decompression, enabling more efficient transmission and processing of large data sets by dynamically adjusting block sizes, effectively handling null values, and controlling error growth in time series data, thus improving the overall performance and accuracy of data transmission over low bandwidth links.
Implementation Method 1
using techniques like Discrete Cosine Transform (DCT) and quantization
Data Source
AI summary
Systems and methods are provided for reducing error in data compression and decompression when data is transmitted over low bandwidth communication links, such as satellite links. Embodiments of the present disclosure provide systems and methods for variable block size compression for gridded data, efficiently storing null values in gridded data, and eliminating growth of error in compressed time series data.