Variable Block Compression for Low-Bandwidth Gridded Data
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, which require efficient compression methods to manage variable data and null values effectively.
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 size adjustment and error tolerance thresholds, to optimize compression while maintaining data accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If data compression is applied to large data sets for transmission over low bandwidth links, then transmission efficiency is improved, but error in compression and decompression increases
Solution Approach 1:
The patent applies segmentation by dividing the data set into multiple blocks and processing each block independently through compression and decompression operations. This allows error containment within individual blocks, preventing error propagation across the entire data set, thereby maintaining reliability while achieving compression efficiency.
Solution Approach 2:
The patent utilizes parameter changes by applying mathematical transformations (such as discrete cosine transform) to convert data between different representations. These parameter transformations enable efficient compression while allowing for controlled error management through inverse transformations during decompression.
2Productivity
If variable block size compression is used to optimize compression efficiency, then productivity is improved, but device complexity increases
Solution Approach 1:
The patent implements dynamics by using variable block sizes that adapt to the characteristics of different data segments. Rather than applying a fixed compression approach, the system dynamically adjusts block dimensions to optimize compression efficiency for each specific data region, achieving higher productivity while managing complexity through adaptive rather than static processing.
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, allows for efficient transmission of large data sets, and ensures accurate representation of scientific data with controlled loss, enhancing the performance of data compression algorithms for gridded scientific data.
Implementation Method 1
using techniques like Discrete Cosine Transform (DCT) and quantization
Implementation Method 2
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.