SATCOM Data Compression System for Aircraft Bandwidth Efficiency
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Solution Overview
Problem
Current SATCOM systems face high costs and limited bandwidth, making broadband connectivity expensive and slow, especially for aircraft-to-ground communications, due to inefficient data compression methods that do not effectively reduce graphical file sizes while maintaining high-fidelity image reproduction.
Innovation Solution
A method of data compression that sorts web page files into lossless and lossy categories, decimates and de-features lossless files, transcodes lossy files, and concatenates all files to achieve a final compression, leveraging commercial algorithms like JPEG2000 and Huffman encoding, to reduce file sizes by up to 2 times while maintaining image quality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If current compression methods are used, then data transfer occurs, but file sizes are not sufficiently reduced and bandwidth efficiency is poor
Solution Approach 1:
The patent segments web page data into distinct categories (graphics files, text files, HTML files, JAVA scripts) and applies different compression techniques to each segment. Graphics files are further divided into lossless and lossy categories with different processing approaches. This segmentation enables optimized compression for each data type, significantly improving overall bandwidth efficiency while reducing total data volume.
2Quantity of substance
If graphical file sizes are reduced through compression, then bandwidth usage decreases, but image fidelity may be compromised
Solution Approach 1:
The patent applies different compression quality levels to different graphical files based on their characteristics and requirements. Lossless compression is applied to graphics where fidelity is critical, while lossy compression (including de-features and transcoding to JPEG2000) is applied to other graphics where some quality reduction is acceptable. This local quality approach optimizes the balance between file size reduction and image fidelity preservation.
3Productivity
If all web page files are concatenated and compressed together, then compression ratio improves, but processing complexity increases
Solution Approach 1:
The patent first segments files by type (graphics, text, HTML, scripts) and applies appropriate compression to each segment. Then it concatenates the compressed segments in a structured manner with proper delimiters and headers. This segmented-then-concatenated approach maintains high compression ratios while managing processing complexity through systematic organization and use of standard compression algorithms.
4Quantity of substance
If lossy files are de-featured and transcoded, then file size reduces significantly, but processing time increases
Solution Approach 1:
The patent performs de-features and transcoding of lossy graphical files as preliminary processing steps before final concatenation and compression. By pre-processing these files to reduce their size and optimize their format early in the pipeline, the overall processing time is managed more efficiently, and the subsequent compression operates on already-optimized data, reducing total processing time while achieving significant file size reduction.
Data Source
AI summary
A method of data compression for compressing a web page with graphics files, text files, JAVA scripts, and HTML files comprises storing the graphics files, the text files, the JAVA scripts, and the HTML files in a temporary directory. The graphics files are sorted into lossless and lossy file groups. The lossy files, the lossless files, the text files, the JAVA scripts, and the HTML files are concatenating and then compressed to yield a compressed web page. The lossy files may be de-featured and transcoded before concatenation. The lossless files may be subsampled or decimated before concatenation to match client display size. Lossy files may be de-featured to match client display size by subsampling and reducing image fidelity. Optimal file concatenation size range is determined to optimally develop compression performance while minimizing latency. The concatenated file size is regulated to the optimal file concatenation size.