Redirecting to original paper in 30 seconds...
Click below to go immediately or wait for automatic redirect
📄 Abstract
Abstract: 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.
Key Contributions
SQ-GAN introduces a novel approach to image compression for semantic/task-oriented communications by integrating semantically driven image coding with vector quantization. It utilizes a semantic-conditioned adaptive mask module (SAMM) to selectively encode relevant features, outperforming existing methods in both perceptual quality and semantic accuracy.
Business Value
Enables more efficient transmission of images for AI-driven applications (e.g., medical diagnosis, autonomous driving) by prioritizing semantically relevant information, reducing bandwidth requirements and improving task performance.