Work

Language-Oriented Semantic Communication for Image Transmission with Fine-Tuned Diffusion Model

Year: 2024

Type: conference-paper

Abstract: Ubiquitous image transmission in emerging applications brings huge overheads to limited wireless resources. Since that text has the characteristic of conveying a large amount of information with very little data, the transmission of the descriptive text of an image can reduce the amount of transmitted data. In this context, this paper develops a novel semantic communication framework based on a text-2-image generative model (Gen-SC). In particular, a transmitter converts the input image to textual modality data. Then the text is transmitted through a noisy channel to the receiver. The receiver then uses the received text to generate images. Additionally, to improve the robustness of text transmission over noisy channels, we designed a transformer-based text transmission codec model. Moreover, we obtained a personalized knowledge base by fine-tuning the diffusion model to meet the requirements of task-oriented transmission scenarios. Simulation results show that the proposed framework can achieve high perceptual quality with reducing the transmitted data volume by up to 99% and is robust to wireless channel noise in terms of portrait image transmission.

Source: International Conference on Wireless Communications and Signal Processing (WCSP)

DOI: 10.1109/wcsp62071.2024.10827002

Authors: Xinfeng Wei, Haonan Tong, Nuocheng Yang, Changchuan Yin

Institution: Beijing University of Posts and Telecommunications

Language: English

Topic: Advanced Wireless Communication Technologies

Subfield: Electrical and Electronic Engineering

Field: Engineering

Domain: Physical Sciences

Open Access status: green

Funders: National Natural Science Foundation of China, China Scholarship Council, Natural Science Foundation of Beijing Municipality, Higher Education Discipline Innovation Project

Awards: G1834021097, G6708062852, G8394501432