AI 中文总结
针对多模态内容重复访问的效率问题，提出ParaJSCC参数化框架，通过离线生成参数包按需传输，降低延迟与传输速率并保持重建质量。
AI 中文摘要
视觉、音频、触觉等多模态信号正日益作为沉浸式通信系统和数字孪生中的持久数字资产被维护。在这些场景中，异构接收端会重复访问相同的多模态内容，且各接收端的模态与带宽需求存在差异。现有压缩方法及联合信源信道编码（JSCC）通常采用按需编码范式，导致重复访问时存在冗余计算与低效率问题。为解决该问题，本文提出ParaJSCC，一种面向可复用表示服务的多模态JSCC框架。该框架在云端/内容服务器端将每个多模态样本离线转换为紧凑的量化参数包，存储在边缘服务节点以实现低延迟访问；服务时仅通过无线信道传输当前请求所需的参数子集，接收端再进行轻量解码。该框架采用渐进式共享-私有参数化，以支持模态选择性传输及不同带宽约束下的可扩展重建。在多模态数据集上的实验表明，ParaJSCC可显著降低在线延迟（例如，仅图像请求的延迟从17.18~ms降至4.34~ms，全多模态请求的延迟从43.96~ms降至11.21~ms），并降低传输速率（选择性请求的传输速率降低47.8%~51.2%），同时在噪声信道下保持良好的重建质量。
英文摘要
Multimodal signals, such as visual, audio, and tactile data, are increasingly maintained as persistent digital assets in immersive communication systems and digital twins. In these settings, the same multimodal content is repeatedly accessed by heterogeneous receivers with varying modality and bandwidth requirements. Existing compression and Joint Source-Channel Coding (JSCC) methods typically follow a per-request encoding paradigm, resulting in redundant computation and low efficiency during repeated access. To address this issue, we propose ParaJSCC, a multimodal JSCC framework designed for reusable representation serving. ParaJSCC converts each multimodal sample offline at the cloud/content server into a compact, quantized parameter package, which is then stored at the edge serving node for low-latency access. During serving, only the subset required by the current request is transmitted over the wireless channel, followed by lightweight decoding at the receiver. The framework employs a progressive shared-private parameterization to support modality-selective transmission and scalable reconstruction under varying bandwidth constraints. Experiments on multimodal datasets show that ParaJSCC significantly reduces online latency (e.g., from 17.18~ms to 4.34~ms for image-only requests and from 43.96~ms to 11.21~ms for full multimodal requests) and transmission rate (by 47.8\%--51.2\% for selective requests), while maintaining strong reconstruction quality under noisy channels.
