Telemetry Data Stream Compression for Low-Delay Lossless Transmission
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Solution Overview
Problem
Current data compression technologies face challenges in achieving high compression ratios with low delay and low computational complexity, especially in real-time telemetry systems where lossless compression is required, as existing algorithms like Lempel-Ziv and Huffman coding are not suitable for real-time applications due to high computational complexity and delay costs.
Innovation Solution
A method that involves selectively classifying data into one-dimensional and imagery data streams, compressing each stream separately using state detection algorithms to categorize segments into different states, and using corresponding encoders for each state, along with prediction models to encode differences, allowing for efficient compression and encryption of data packets for transmission.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If lossless compression algorithms like Lempel-Ziv or Huffman coding are used, then data integrity is preserved, but computational complexity and processing delay increase significantly
Solution Approach 1:
The patent segments the telemetry data stream into multiple parallel compression streams, each processed by a dedicated compression engine implementing a simplified version of Lempel-Ziv. This segmentation distributes the computational load across multiple processors, reducing the complexity burden on any single processing unit while maintaining lossless compression capabilities for the entire data set.
Solution Approach 2:
The patent performs preliminary classification and filtering of telemetry data to identify and separate high-value data elements that require lossless compression from lower-value data that can use lossy compression. This preliminary action allows the system to apply computationally intensive lossless algorithms only where necessary, reducing overall computational complexity while preserving data integrity for critical parameters.
2Productivity
If high compression ratio is achieved, then data transmission efficiency improves, but processing delay increases
Solution Approach 1:
The patent implements dynamic adjustment of compression parameters based on real-time telemetry data characteristics and bandwidth conditions. The system dynamically switches between different compression strategies (lossless vs. lossy, different compression ratios) and adjusts buffer sizes to optimize the balance between compression ratio and processing delay, ensuring high transmission efficiency without excessive delay.
Solution Approach 2:
The patent divides the telemetry data stream into multiple parallel compression streams processed simultaneously by dedicated compression engines. Each engine handles a portion of the data with optimized compression parameters, enabling the system to achieve high overall compression ratios while maintaining low per-stream processing delays through parallel execution.
3Measurement precision
If lossless compression is applied to all data streams, then data accuracy is maintained, but compression ratio decreases
Solution Approach 1:
The patent applies different compression strategies to different segments of the telemetry data based on their individual characteristics and importance. Critical sensor data requiring high accuracy receives lossless compression, while less critical auxiliary data uses lossy compression with adjustable fidelity. This local differentiation maintains data accuracy where needed while achieving higher overall compression ratios.
Solution Approach 2:
The patent dynamically changes compression parameters including compression type (lossless/lossy), compression ratio, and buffer sizes based on real-time analysis of data characteristics, bandwidth availability, and latency requirements. This parameter adaptation enables the system to optimize the balance between data accuracy and compression ratio for different operating conditions.
Data Source
AI summary
A method and system for transmitting data is disclosed. The method may include receiving data from two or more data sources. The method may also include selectively classifying the data into at least two input data streams, the at least two data streams including a one dimensional data stream, called an encoded data stream, and an imagery data stream, which can include still images or video or both. The method may also include separately compressing the one-dimensional data stream into a first compressed bit-stream and the imagery data stream into a second compressed bit-stream and combining the first compressed bit-stream and the second compressed bit-stream into a packetized bit-stream. The method may also include encrypting the packetized bit-stream to generate encrypted data packets prepared for transmission.