Multimodel Telemetry Compression for Low-Latency Lossless Encoding
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Solution Overview
Problem
Current data compression techniques for telemetry systems face challenges in achieving high compression ratios while maintaining low latency and low computational complexity, especially in real-time applications where lossless compression is required, as existing algorithms often require the entire dataset to process and incur significant delay and resource costs.
Innovation Solution
The development of block-based compression algorithms that process data in real-time by classifying input data into predefined categories and applying optimal compression methods, allowing for parallel processing and reducing computation complexity, power consumption, and memory requirements, while achieving high compression ratios with low latency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If lossless compression algorithms are used to achieve high compression ratios, then data integrity is maintained, but transmission delay and computational complexity increase significantly
Solution Approach 1:
The patent divides the compression process into multiple passes: a first pass that processes data with higher complexity to achieve better compression, and a second pass that uses simpler algorithms for final compression. This segmentation allows the system to balance between compression ratio and processing time by distributing computational load across different stages.
Solution Approach 2:
The patent performs preliminary data processing and analysis before the main compression operation. By pre-processing the data to identify patterns and characteristics, the system can apply more efficient compression strategies during the actual compression phase, reducing overall processing time while maintaining compression effectiveness.
2Loss of information
If traditional lossless compression algorithms are used, then compression ratio is improved, but computational complexity and power consumption increase
Solution Approach 1:
The patent implements dynamic algorithm selection that adapts to the characteristics of the input data. Based on data analysis, the system dynamically chooses the most appropriate compression algorithm from multiple options, optimizing the balance between compression ratio and computational complexity for different data types and patterns.
Solution Approach 2:
The patent changes compression parameters and algorithm selection based on the analyzed characteristics of the input data. By adjusting parameters such as compression level, algorithm type, and processing depth according to data properties, the system achieves high compression ratios while avoiding unnecessary computational overhead.
3Loss of information
If high compression ratios are achieved, then bandwidth savings increase, but processing time and memory requirements increase
Solution Approach 1:
The patent segments the compression process into multiple passes with different objectives. The first pass focuses on achieving high compression ratios for maximum bandwidth savings, while the second pass optimizes for processing speed. This segmentation allows the system to deliver high compression performance without requiring all processing to occur at maximum computational intensity simultaneously.
Data Source
AI summary
Methods and systems for processing telemetry data that contains multiple data types is disclosed. Optimum multimodal encoding approaches can be used which can achieve data-specific compression performance for heterogeneous datasets by distinguishing data types and their characteristics at real-time and applying most effective compression method to a given data type. Using an optimum encoding diagram for heterogeneous data, a data classification algorithm classifies input data blocks into predefined categories, such as Unicode, telemetry, RCS and IR for telemetry datasets, and a class of unknown which includes non-studied data types, and then assigns them into corresponding compression models.