Digital Semantic Communications with Variable Product Quantization for Image Transmission
Junxiao Liang, Fengyu Wang, Yuan Zheng, Wenjun Xu, Xiaodong Xu, Jincheng Dai · 2025
Semantic communications (SemCom) is considered one of the key technologies for next-generation communications. However, most SemCom systems utilize Deep Learning (DL) based joint source-channel coding (JSCC), which are incompatible with existing digital communication systems. In this paper, we propose a novel digital SemCom system based on variable product quantization (VPQ-SemCom), which harnesses multiple lightweight codebooks to represent images and dynamically optimize bitrates according to the entropy of semantic features to adapt to transmission scenarios with multiple bandwidths and SNRs. Specifically, product quantization (PQ), which can represent semantic features with several lightweight codebooks, is introduced to provide powerful representation capacities of semantic features. Furthermore, a rate adaption module, which can flexibly adjust feature length based on the entropy of semantic features, is proposed to integrate with PQ to improve rate-distortion performance. The experimental results demonstrate that VPQ-SemCom shows 32.4% improvement at high SNRs and 62.2% improvement at SNR = 2dB in Learned Perceptual Image Patch Similarity (LPIPS) compared to current state-of-the-art vector quantization (VQ) based digital SemCom systems.