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Belief Propagation Optimization for Lossy Compression Based on Gaussian Source
Huan Deng1,2, Dan Song1, Zhiping Xu3,4
1Navigation Collage, Jimei University, Xiamen 361021, China.
Sensors (Basel, Switzerland)
|November 14, 2023
Summary
This study introduces optimized belief propagation (BP) algorithms for efficient lossy compression of sensor data in the Internet of Things. These algorithms improve data compression performance by mitigating issues related to trapping sets in low-density parity-check codes.
Area of Science:
- Information Theory
- Data Compression
- Signal Processing
Background:
- Internet of Things (IoT) sensor nodes require efficient data compression for storage.
- Lossy compression is crucial for handling continuous environmental data streams.
- Existing compression schemes can be improved for higher efficiency.
Purpose of the Study:
- To develop high-efficiency lossy compression techniques for continuous data sources.
- To apply the duality principle between lossy source coding and channel decoding.
- To introduce and optimize the belief propagation (BP) algorithm for Gaussian sources.
Main Methods:
- Implemented lossy source coding based on the duality principle.
- Utilized the belief propagation (BP) algorithm for compression of Gaussian sources.
- Proposed optimized BP algorithms to address performance limitations caused by trapping sets in protograph low-density parity-check (P-LDPC) codes.
Main Results:
- The optimized BP algorithms effectively weaken the impact of trapping sets.
- Improved distortion-rate performance was achieved using the proposed algorithms.
- Simulation results validate the enhanced compression efficiency.
Conclusions:
- Optimized BP algorithms offer superior performance for lossy source coding in IoT applications.
- The duality principle provides a novel framework for efficient data compression.
- Mitigating trapping set effects is key to enhancing BP-based compression.
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