# SQ-GAN: Semantic Image Communications Using Masked Vector Quantization

arXiv: https://arxiv.org/abs/2502.09520 · HTML: https://mlsanity.com/paper/2502.09520/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 30 of its 46 references that are in the map.
- First posted on arXiv 13 Feb 2025.

## Builds on (most cited first, 30 of 30)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [The Unreasonable Effectiveness of Deep Features as a Perceptual Metric](https://mlsanity.com/paper/1801.03924/) (2018)
- [Rethinking the Inception Architecture for Computer Vision](https://mlsanity.com/paper/1512.00567/) (2015)
- [Neural Discrete Representation Learning](https://mlsanity.com/paper/1711.00937/) (2017)
- [Image-to-Image Translation with Conditional Adversarial Networks](https://mlsanity.com/paper/1611.07004/) (2016)
- [The Cityscapes Dataset for Semantic Urban Scene Understanding](https://mlsanity.com/paper/1604.01685/) (2016)
- [Taming Transformers for High-Resolution Image Synthesis](https://mlsanity.com/paper/2012.09841/) (2020)
- [Semantic Image Synthesis with Spatially-Adaptive Normalization](https://mlsanity.com/paper/1903.07291/) (2019)
- [Empirical Evaluation of Rectified Activations in Convolutional Network](https://mlsanity.com/paper/1505.00853/) (2015)
- [InternImage: Exploring Large-Scale Vision Foundation Models with Deformable Convolutions](https://mlsanity.com/paper/2211.05778/) (2022)
- [High-Fidelity Generative Image Compression](https://mlsanity.com/paper/2006.09965/) (2020)
- [Deep Learning Enabled Semantic Communication Systems](https://mlsanity.com/paper/2006.10685/) (2020)
- [Beyond Transmitting Bits: Context, Semantics, and Task-Oriented Communications](https://mlsanity.com/paper/2207.09353/) (2022)
- [6G Networks: Beyond Shannon Towards Semantic and Goal-Oriented Communications](https://mlsanity.com/paper/2011.14844/) (2020)
- [Semantic Communication Systems for Speech Transmission](https://mlsanity.com/paper/2102.12605/) (2021)
- [Semantic Communications for Future Internet: Fundamentals, Applications, and Challenges](https://mlsanity.com/paper/2207.00427/) (2022)
- [Task-Oriented Multi-User Semantic Communications](https://mlsanity.com/paper/2112.10255/) (2021)
- [Deep Joint Source-Channel Coding for Semantic Communications](https://mlsanity.com/paper/2211.08747/) (2022)
- [SSD-GAN: Measuring the Realness in the Spatial and Spectral Domains](https://mlsanity.com/paper/2012.05535/) (2020)
- [Fidelity-Controllable Extreme Image Compression with Generative Adversarial Networks](https://mlsanity.com/paper/2008.10314/) (2020)
- [Artificial Intelligence-Enabled Intelligent 6G Networks](https://mlsanity.com/paper/1912.05744/) (2019)
- [Not All Image Regions Matter: Masked Vector Quantization for Autoregressive Image Generation](https://mlsanity.com/paper/2305.13607/) (2023)
- [Wireless Network Digital Twin for 6G: Generative AI as A Key Enabler](https://mlsanity.com/paper/2311.17451/) (2023)
- [Generative AI for Physical Layer Communications: A Survey](https://mlsanity.com/paper/2312.05594/) (2023)
- [Semantic Communications Based on Adaptive Generative Models and Information Bottleneck](https://mlsanity.com/paper/2309.02387/) (2023)
- [Evolving Semantic Communication with Generative Model](https://mlsanity.com/paper/2403.20237/) (2024)
- [Road images augmentation with synthetic traffic signs using neural networks](https://mlsanity.com/paper/2101.04927/) (2021)

## Built on by (most cited first, 4 of 4)

- [Baseline-Relative Counterfactual Refinement for Bit-Aware Visual Token Communication](https://mlsanity.com/paper/2608.16192/) (2026)
- [Efficient Semantic Image Communication for Traffic Monitoring at the Edge](https://mlsanity.com/paper/2604.12622/) (2026)
- [Foundation Models for Generalizable Semantic and Goal-Oriented Communication](https://mlsanity.com/paper/2609.07853/) (2026)
- [Selective Amortization of Full-Budget Counterfactual Reasoning for Visual Token Communication](https://mlsanity.com/paper/2609.30756/) (2026)

## Abstract (arXiv)

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.