Lossless Telemetry Compression with Data Classification and Shuffling
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Solution Overview
Problem
Existing data compression technologies for real-time telemetry systems face challenges in achieving high compression ratios with low delay and low computational complexity, especially when lossless compression is required, as current algorithms like Lempel-Ziv and Huffman coding may not reach sufficient compression ratios and incur high computational costs.
Innovation Solution
The method involves classifying data into PCM-encoded and image data streams, separately compressing them using low-delay low-complexity lossless compression techniques, and shuffling the compressed bit-streams to introduce randomness, allowing for higher compression ratios while maintaining low delay and complexity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If known compression algorithms (Lempel-Ziv, Huffman coding) are used, then data can be compressed, but the compression ratio is insufficient and computational complexity is high
Solution Approach 1:
The patent segments the compressed bitstream into multiple groups and applies different randomization operations to each group. This segmentation allows the system to achieve higher effective compression ratios by preventing pattern repetition across group boundaries, while keeping the computational complexity manageable through localized processing of each segment rather than the entire bitstream at once
Solution Approach 2:
The patent introduces dynamic randomization operations that vary the bitstream arrangement based on group position and size. This dynamic approach adapts the compression strategy to different data characteristics and transmission requirements, enabling higher compression ratios without requiring a single fixed complex algorithm to handle all cases
2Productivity
If lossless compression is applied to achieve high compression ratio, then data integrity is maintained, but delay and computational cost increase
Solution Approach 1:
By segmenting the bitstream into smaller groups for independent randomization processing, the patent reduces the time required for each compression operation. This segmentation enables parallel processing potential and reduces the cumulative delay that would result from processing the entire data stream sequentially with traditional lossless algorithms
Solution Approach 2:
The patent applies randomization operations selectively to different groups rather than uniformly to all data. This partial action approach focuses computational resources on critical segments, achieving sufficient compression ratios without the excessive processing delay that would result from applying full-strength lossless compression to every byte of data
3Productivity
If data is compressed without classification, then processing is simpler, but compression efficiency is reduced
Solution Approach 1:
The patent segments data into distinct groups based on characteristics such as data type or source, allowing each group to receive optimized compression treatment. This segmentation improves compression efficiency by tailoring the randomization strategy to each group's specific patterns, while the modular group-based structure keeps processing complexity manageable through standardized handling of each segment
Data Source
AI summary
A method for compressing data is disclosed. The method may include receiving data from one or more data sources. The method may also include selectively classifying the data into one or more data streams, the one or more data streams including at least PCM-encoded data and image data. The method may further include separately compressing the PCM-encoded data and the image data into first and second compressed bit-streams. The method may also include shuffling the first and second compressed bit-streams.