研究人员开发了一种用于无线图像传输的轻量级联合信源信道编码（JSCC）新框架。该框架利用选择性地将标准卷积层替换为深度分离卷积（DSConv）层。研究探讨了在不同位置和比例替换层的影响，发现中间层替换在复杂度和性能之间取得了良好的平衡。 AI
影响 提供了一种降低图像传输系统计算复杂度的方法，有利于资源受限的边缘设备。
排序理由 这是一篇详细介绍图像传输新框架的研究论文。
AI 生成摘要 · Google Gemini · 来自 2 个来源。 我们如何撰写摘要 →
研究人员提出使用选择性深度分离卷积的轻量级JSCC框架
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- 这是一篇详细介绍图像传输新框架的研究论文。
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报道来源 [2]
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选择性深度可分离卷积用于无线图像传输中的轻量级联合信源信道编码
arXiv:2604.22338v1 Announce Type: cross Abstract: Depthwise separable convolutional (DSConv) layers have been successfully applied to deep learning (DL)-based joint source-channel coding (JSCC) schemes to reduce computational complexity. However, a systematic investigation of the…
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用于无线图像传输轻量级联合信源信道编码的选择性深度可分离卷积
Depthwise separable convolutional (DSConv) layers have been successfully applied to deep learning (DL)-based joint source-channel coding (JSCC) schemes to reduce computational complexity. However, a systematic investigation of the layerwise and ratio-wise replacement of standard …