Selective Data Compression for Bandwidth-Limited Satellite Transmission
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Solution Overview
Problem
Satellite data transmission is limited by bandwidth constraints, necessitating efficient compression techniques that can selectively prioritize and compress data based on content and relevance to optimize storage and transmission without losing valuable information.
Innovation Solution
A system comprising processors and memory modules that analyze data to determine appeal factor values based on content, context, and application, allowing for the selection of appropriate compression configurations and algorithms to compress data selectively, prioritizing the preservation of important information while reducing overall data size.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If data compression is applied to reduce data size for satellite transmission, then bandwidth utilization is improved, but data quality and information preservation deteriorate
Solution Approach 1:
The patent applies different compression configurations to different portions of data based on their importance. Critical data portions use compression settings that preserve quality, while less critical portions use more aggressive compression. This resolves the contradiction by making compression quality spatially variable rather than uniform, thus maintaining overall data quality while achieving better bandwidth utilization.
Solution Approach 2:
The system dynamically changes compression parameters based on data characteristics and importance. By adjusting compression ratios, algorithms, and settings according to the specific data being transmitted, the system optimizes the balance between compression efficiency and quality preservation, resolving the contradiction between bandwidth utilization and information loss.
2Loss of information
If selective compression is applied to prioritize important data, then information preservation is improved, but processing complexity increases
Solution Approach 1:
The patent segments data into different portions based on importance or characteristics, then applies appropriate compression configurations to each segment. This segmentation approach simplifies the processing complexity by breaking down the complex decision-making into manageable segments, while still achieving selective preservation of important information.
Solution Approach 2:
The system performs preliminary analysis of data to identify important portions before applying compression. By pre-classifying data and determining compression strategies in advance, the system reduces the complexity of real-time processing while ensuring that critical information is preserved during compression.
3Productivity
If compression algorithms are selected based on data content analysis, then compression efficiency is improved, but computational time increases
Solution Approach 1:
The patent applies partial analysis to data portions, focusing computational effort on identifying key characteristics rather than exhaustive analysis. This allows the system to select appropriate compression algorithms efficiently without spending excessive computation time on complete data analysis, thus resolving the contradiction between compression efficiency and computational time.
Data Source
AI summary
Apparatuses, methods and systems for selective data compression are described. Apparatuses for selective data compression comprise one or modules executable to analyze data to generate analysis results, determine and associate appeal factor values with the data or portions thereof, and compress the data with the compression configurations associated with the appeal factor values. The apparatuses may be employed to compress data on-board manned or unmanned aerial vehicles or other devices. Through use of data analysis, including but not limited to artificial intelligence analysis, image analysis, meta-data analysis, in orbit analysis, and so forth, the manned or unmanned aerial vehicles may compress data collected, generated and/or stored in the vehicle based on data content, data contextual information, data collection opportunity, a priori information, change detection, and/or a particular task the vehicle is tasked to perform (application).