Sparse Transform Compression for Real-Time IoT Data Streams
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Solution Overview
Problem
Existing data compression techniques for real-time applications, particularly in IoT devices, face challenges in achieving high compression ratios while minimizing computational complexity, delay, and resource consumption, especially when lossless compression is required, due to the need for exploring entire data sets and high computational complexity of algorithms like Lempel-Ziv and Huffman coding.
Innovation Solution
A lossless data compression method using sparse transform extraction, transform matrix modeling, and adaptive entropy coding, which reduces bandwidth requirements and computation complexity by identifying redundancy in IoT data through sparse transforms and efficient modeling, allowing for real-time processing and parallel processing capabilities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If lossless compression algorithms like Lempel-Ziv or Huffman coding are used, then data compression is achieved, but computational complexity and delay costs increase making real-time processing impossible
Solution Approach 1:
The patent segments the data processing into distinct stages: sparse transform extraction, transform matrix modeling, transform coefficient modeling, and entropy coding. Each stage processes specific aspects of the data independently, allowing for optimized computation at each step rather than applying complex algorithms to the entire dataset at once.
Solution Approach 2:
The patent extracts only the essential features from the original data through sparse transform, identifying and retaining only the most significant transform coefficients. This extraction process removes redundant information while preserving the core data characteristics, enabling compression without requiring complex processing of all original data elements.
2Productivity
If high compression ratio is achieved, then data transfer rate increases, but resource consumption (time, computing power, power) increases
Solution Approach 1:
The patent changes the representation parameters of the data by transforming it into a sparse domain where most coefficients are zero or near-zero. This parameter transformation allows for efficient compression by focusing computational resources only on the non-zero coefficients, significantly reducing power consumption while maintaining high compression ratios.
Solution Approach 2:
The patent applies compression processing selectively to only the most significant portions of the data (non-zero transform coefficients) rather than processing every data element equally. This partial action approach achieves effective compression with reduced computational effort and lower power consumption.
3Loss of information
If entire data set is explored to build dictionary during compression, then compression is achieved, but real-time processing becomes impossible due to delay
Solution Approach 1:
The patent performs preliminary sparse transform extraction and identifies significant coefficients before the main compression process. By pre-processing the data to highlight important features, the system avoids the need to explore and analyze the entire dataset during compression, enabling real-time processing with minimal delay.
Solution Approach 2:
The patent extracts only the essential data characteristics through sparse transform, working with a simplified representation rather than the complete original dataset. This extraction enables compression to proceed quickly on the reduced data structure while still achieving effective compression ratios.
Data Source
AI summary
A disclosure for lossless data compression can include receiving a data block by a processor, performing, by the processor, a sparse transform extraction on the data block, selecting, by the processor, a transform matrix for the data block, modeling, by the processor, the selected transform matrix for the data block, selecting, by the processor, a transform coefficient model for the data block, modeling, by the processor, the selected transform coefficient model for the data block, compressing, by the processor, the data in the data block using the selected transform matrix and the selected transform coefficient model.