Lossless Telemetry Compression with Stream Separation 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 computational complexity and delay, particularly in applications requiring lossless compression, as current algorithms like Lempel-Ziv and Huffman coding are not sufficient for high compression ratios and incur significant computational costs.
Innovation Solution
A method involving data classification into PCM-encoded and image data streams, followed by separate lossless compression using prediction models and entropy encoding, and shuffling of compressed bit-streams to introduce randomness, allowing for higher compression ratios while maintaining low complexity and delay.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If lossless compression algorithms (Lempel-Ziv, Huffman coding) are used, then data integrity is preserved, but compression ratio is insufficient and computational complexity is high
Solution Approach 1:
The patent segments the compression process into multiple passes: a first pass that identifies and encodes recurring data patterns with pointers, and a second pass that applies entropy encoding to the residual data. This segmentation allows the system to achieve higher compression ratios by handling different types of redundancy separately, while maintaining lossless compression through the combination of both passes.
Solution Approach 2:
The patent changes the parameter of compression approach from single-pass traditional algorithms to multi-pass adaptive encoding. The first pass transforms the data by replacing recurring patterns with pointers (changing the data representation), and the second pass applies entropy encoding with adaptive probability models. This parameter change enables achieving compression ratios of 3:1 or greater while maintaining lossless reconstruction.
2Device complexity
If traditional compression algorithms are used, then implementation is simple, but delay costs are high due to computational complexity
Solution Approach 1:
The patent applies preliminary action by performing pattern identification and pointer encoding in a first pass before the actual compression encoding in the second pass. The system pre-processes the data to identify recurring patterns and replace them with pointers, which simplifies the subsequent entropy encoding step. This preliminary action reduces the computational burden during real-time compression, thereby reducing delay costs while maintaining implementation feasibility.
3Productivity
If high compression ratio is achieved, then data transfer rate increases, but computational resources and time consumption increase
Solution Approach 1:
The patent extracts and handles the most compressible elements (recurring patterns) in a first pass by replacing them with pointers. This extraction removes the bulk of the redundancy from the data stream before applying entropy encoding in the second pass. By taking out the easily compressible patterns first, the system achieves high compression ratios without requiring excessive computational resources during the main encoding process, as the difficult patterns are already minimized.
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.