Semantic communication is an emerging approach that enhances transmission efficiency by conveying the semantic content of information more effectively. It has garnered significant attention in recent years. However, existing semantic communication systems for image transmission typically adopt direct transmission of features or uniformly compress features before transmission. They have not yet considered the differential impact of features on image recovery at the receiver end and the issue of bandwidth limitations during actual transmission. This paper shows that non-uniform processing of features leads to better image recovery under bandwidth constraints compared to uniform processing. Based on this, we propose a semantic communication system for image transmission, which introduces non-uniform quantization techniques. In the feature transmission stage, the system performs varying levels of quantization based on the differences in feature performance at the receiver, thereby reducing the bandwidth requirement. Inspired by quantitative quantization techniques, we design a non-uniform quantization algorithm capable of dynamic bit allocation. This algorithm, under bandwidth constraints, dynamically adjusts the quantization precision of features based on their contribution to the completion of tasks at the receiver end, ensuring the quality and accuracy of the transmitted data even under limited bandwidth conditions. Experimental results show that the proposed system reduces bandwidth usage while ensuring image reconstruction quality.

The Alternating Direction Method of Multipliers (ADMM) has proven to be an efficient approach for implementing linear programming (LP) decoding of low-density parity-check (LDPC) codes. By introducing penalty terms into the LP decoding model’s objective function, ADMM-based variable node penalized decoding effectively mitigates non-integral solutions, thereby improving frame error rate (FER) performance, especially in the low signal-to-noise ratio (SNR) region. In this paper, we leverage the ADMM framework to derive explicit iterative steps for solving the LP decoding problem for LDPC codes with penalty functions. To further enhance decoding efficiency and accuracy, We propose an LDPC code decoding algorithm based on the symmetric ADMM (S-ADMM). We also establish some contraction properties satisfied by the iterative sequence of the algorithm. Through simulation experiments, we evaluate the proposed S-ADMM decoder using three standard LDPC codes and three representative fifth-generation (5G) codes. The results show that the S-ADMM decoder consistently outperforms conventional ADMM penalized decoders, offering significant improvements in decoding performance.