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Researchers propose lightweight JSCC framework using selective depthwise separable convolutions

By PulseAugur Editorial · Summary by gemini-2.5-flash-lite from 2 sources

Researchers have developed a new framework for lightweight joint source-channel coding (JSCC) in wireless image transmission. This framework utilizes selective replacement of standard convolutional layers with depthwise separable convolutional (DSConv) layers. The study investigates the impact of replacing layers at different positions and ratios, finding that intermediate layer replacements offer a good balance between complexity and performance. AI

Summary written by gemini-2.5-flash-lite from 2 sources. How we write summaries →

IMPACT Offers a method for reducing computational complexity in image transmission systems, benefiting resource-constrained edge devices.

RANK_REASON This is a research paper detailing a new framework for image transmission.

Read on arXiv cs.CV →

COVERAGE [2]

  1. arXiv cs.CV TIER_1 · Ming Ye, Kui Cai, Cunhua Pan, Zhen Mei, Wanting Yang, Chunguo Li ·

    Selective Depthwise Separable Convolution for Lightweight Joint Source-Channel Coding in Wireless Image Transmission

    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…

  2. arXiv cs.CV TIER_1 · Chunguo Li ·

    Selective Depthwise Separable Convolution for Lightweight Joint Source-Channel Coding in Wireless Image Transmission

    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 …

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