# Semantic Feature Decomposition based Semantic Communication System of Images with Large-scale Visual Generation Models

arXiv: https://arxiv.org/abs/2410.20126 · HTML: https://mlsanity.com/paper/2410.20126/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 27 references that are in the map.
- First posted on arXiv 26 Oct 2024.

## Builds on (most cited first, 17 of 17)

- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020)
- [High-Resolution Image Synthesis with Latent Diffusion Models](https://mlsanity.com/paper/2112.10752/) (2021)
- [Segment Anything](https://mlsanity.com/paper/2304.02643/) (2023)
- [Adding Conditional Control to Text-to-Image Diffusion Models](https://mlsanity.com/paper/2302.05543/) (2023)
- [BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generation](https://mlsanity.com/paper/2201.12086/) (2022)
- [CLIPScore: A Reference-free Evaluation Metric for Image Captioning](https://mlsanity.com/paper/2104.08718/) (2021)
- [Towards Robust Monocular Depth Estimation: Mixing Datasets for Zero-shot Cross-dataset Transfer](https://mlsanity.com/paper/1907.01341/) (2019)
- [Wasserstein GAN](https://mlsanity.com/paper/1701.07875/) (2017)
- [DeepJSCC-f: Deep Joint Source-Channel Coding of Images with Feedback](https://mlsanity.com/paper/1911.11174/) (2019)
- [Semantic Communications for Future Internet: Fundamentals, Applications, and Challenges](https://mlsanity.com/paper/2207.00427/) (2022)
- [Deep Learning for Joint Source-Channel Coding of Text](https://mlsanity.com/paper/1802.06832/) (2018)
- [Text + Sketch: Image Compression at Ultra Low Rates](https://mlsanity.com/paper/2307.01944/) (2023)
- [Optimization of Image Transmission in a Cooperative Semantic Communication Networks](https://mlsanity.com/paper/2301.00433/) (2023)
- [A Specific Task-oriented Semantic Image Communication System for substation patrol inspection](https://mlsanity.com/paper/2301.03331/) (2023)
- [Semantic Similarity Score for Measuring Visual Similarity at Semantic Level](https://mlsanity.com/paper/2406.03865/) (2024)

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

- [Generative Video Semantic Communication via Multimodal Semantic Fusion with Large Model](https://mlsanity.com/paper/2502.13838/) (2025)
- [DeepSeek-Inspired Exploration of RL-based LLMs and Synergy with Wireless Networks: A Survey](https://mlsanity.com/paper/2503.09956/) (2025)
- [Generative Semantic Communication: Architectures, Technologies, and Applications](https://mlsanity.com/paper/2412.08642/) (2024)
- [A Survey on Semantic Communication for Vision: Categories, Frameworks, Enabling Techniques, and Applications](https://mlsanity.com/paper/2601.22202/) (2026)

## Abstract (arXiv)

The end-to-end image communication system has been widely studied in the academic community. The escalating demands on image communication systems in terms of data volume, environmental complexity, and task precision require enhanced communication efficiency, anti-noise ability and semantic fidelity. Therefore, we proposed a novel paradigm based on Semantic Feature Decomposition (SeFD) for the integration of semantic communication and large-scale visual generation models to achieve high-performance, highly interpretable and controllable image communication. According to this paradigm, a Texture-Color based Semantic Communication system of Images TCSCI is proposed. TCSCI decomposing the images into their natural language description (text), texture and color semantic features at the transmitter. During the transmission, features are transmitted over the wireless channel, and at the receiver, a large-scale visual generation model is utilized to restore the image through received features. TCSCI can achieve extremely compressed, highly noise-resistant, and visually similar image semantic communication, while ensuring the interpretability and editability of the transmission process. The experiments demonstrate that the TCSCI outperforms traditional image communication systems and existing semantic communication systems under extreme compression with good anti-noise performance and interpretability.