In the new paradigm of semantic communication (SC), the focus is on
delivering meanings behind bits by extracting semantic information from raw
data. Recent advances in data-to-text models facilitate language-oriented SC,
particularly for text-transformed image communication via image-to-text (I2T)
encoding and text-to-image (T2I) decoding. However, although semantically
aligned, the text is too coarse to precisely capture sophisticated visual
features such as spatial locations, color, and texture, incurring a significant
perceptual difference between intended and reconstructed images. To address
this limitation, in this paper, we propose a novel language-oriented SC
framework that communicates both text and a compressed image embedding and
combines them using a latent diffusion model to reconstruct the intended image.
Experimental results validate the potential of our approach, which transmits
only 2.09\% of the original image size while achieving higher perceptual
similarities in noisy communication channels compared to a baseline SC method
that communicates only through text.The code is available at
this https URL .

基于语言的语义通信框架通过语言 - 图像编码和图像 - 语言解码，以及潜在扩散模型，实现了在嘈杂通信渠道中以较高的感知相似度重建预期图像的能力。

面向语言的图像传输的语义潜空间表示

Language-Oriented Semantic Latent Representation for Image Transmission

Current communication technologies face limitations in terms of theoretical
capacity, spectrum availability, and power resources. Pragmatic communication,
leveraging terminal intelligence for selective data transmission, offers
resource conservation. Existing research lacks universal intention resolution
tools, limiting applicability to specific tasks. This paper proposes an image
pragmatic communication framework based on a Pragmatic Agent for Communication
Efficiency (PACE) using Large Language Models (LLM). In this framework, PACE
sequentially performs semantic perception, intention resolution, and
intention-oriented coding. To ensure the effective utilization of LLM in
communication, a knowledge base is designed to supplement the necessary
knowledge, dedicated prompts are introduced to facilitate understanding of
pragmatic communication scenarios and task requirements, and a chain of thought
is designed to assist in making reasonable trade-offs between transmission
efficiency and cost. For experimental validation, this paper constructs an
image pragmatic communication dataset along with corresponding evaluation
standards. Simulation results indicate that the proposed method outperforms
traditional and non-LLM-based pragmatic communication in terms of transmission
efficiency.

提出了基于 Pragmatic Agent for Communication Efficiency (PACE) 和 Large Language Models (LLM) 的图像实用通信框架，该方法在传输效率方面优于传统和非 LLM 的实用通信。