This work introduces Semantically Masked Vector Quantized Generative Adversarial Network (SQ-GAN), a novel approach integrating semantically driven image coding and vector quantization to optimize image compression for semantic/task-oriented communications. The method only acts on source coding and is fully compliant with legacy systems. The semantics is extracted from the image computing its semantic segmentation map using off-the-shelf software. A new specifically developed semantic-conditioned adaptive mask module (SAMM) selectively encodes semantically relevant features of the image. The relevance of the different semantic classes is task-specific, and it is incorporated in the training phase by introducing appropriate weights in the loss function. SQ-GAN outperforms state-of-the-art image compression schemes such as JPEG2000, BPG, and deep-learning based methods across multiple metrics, including perceptual quality and semantic segmentation accuracy on the reconstructed image, at extremely low compression rates.
@article{arxiv.2502.09520,
title = {SQ-GAN: Semantic Image Communications Using Masked Vector Quantization},
author = {Francesco Pezone and Sergio Barbarossa and Giuseppe Caire},
journal= {arXiv preprint arXiv:2502.09520},
year = {2025}
}
Comments
arXiv admin note: substantial text overlap with arXiv:2502.01675