High-Efficiency Lossy Source Coding Based on Multi-Layer Perceptron Neural Network

With the rapid growth of data volume in sensor networks, lossy source coding systems achieve high-efficiency data compression with low distortion under limited transmission bandwidth. However, conventional compression algorithms rely on a two-stage framework with high computational complexity and frequently struggle to balance compression performance with generalization ability. To address these issues, an end-to-end lossy compression method is proposed in this paper. The approach integrates an enhanced belief propagation algorithm with a multi-layer perceptron neural network, aiming to introduce a novel joint optimization architecture described as "encoding-structured encoding-decoding". In addition, a quantization module incorporating random perturbation and the straight-through estimator is designed to address the non-differentiability in the quantization process. Simulation results demonstrate that the proposed system significantly improves compression performance while offering superior generalization and reconstruction quality. Furthermore, the designed neural architecture is both simple and efficient, reducing system complexity and enhancing feasibility for practical deployment.