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<p>Remote monitoring of transmission lines plays a vital role in ensuring the stable operation of power systems, especially in regions with weak or unstable network signals, where efficient data transmission and storage are essential. However, traditional image compression methods face significant limitations in both quality and efficiency when applied to high-resolution imagery in such scenarios.To address these challenges, this paper proposes a deep learning–based image compression approach incorporating an Efficient Channel-Temporal Attention Module (ETAM). The ETAM module integrates Efficient Channel Attention (ECA-Net) and a Temporal Attention Module (TAM) to jointly enhance the extraction of spatial and temporal features, thereby improving compression efficiency and reconstruction quality.Experimental results demonstrate that the proposed method consistently outperforms both traditional and state-of-the-art deep learning–based compression techniques across multiple evaluation metrics, including PSNR, SSIM, and LPIPS. Notably, evaluations on the STN PLAD dataset show that ETAM better preserves fine-grained details and textures, even under high compression ratios, resulting in reconstructions that closely resemble the original images.These findings underscore the practical potential of the ETAM method for efficient, high-quality image compression in real-world applications such as transmission line monitoring under constrained network conditions.</p>

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Deep Learning Image Compression Method Based On Efficient Channel-Time Attention Module

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年5月5日

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  • Xiu Ji,
  • Xiao Yang,
  • Zheyu Yue,
  • Hongliu Yang,
  • Boyang Zheng

摘要

Remote monitoring of transmission lines plays a vital role in ensuring the stable operation of power systems, especially in regions with weak or unstable network signals, where efficient data transmission and storage are essential. However, traditional image compression methods face significant limitations in both quality and efficiency when applied to high-resolution imagery in such scenarios.To address these challenges, this paper proposes a deep learning–based image compression approach incorporating an Efficient Channel-Temporal Attention Module (ETAM). The ETAM module integrates Efficient Channel Attention (ECA-Net) and a Temporal Attention Module (TAM) to jointly enhance the extraction of spatial and temporal features, thereby improving compression efficiency and reconstruction quality.Experimental results demonstrate that the proposed method consistently outperforms both traditional and state-of-the-art deep learning–based compression techniques across multiple evaluation metrics, including PSNR, SSIM, and LPIPS. Notably, evaluations on the STN PLAD dataset show that ETAM better preserves fine-grained details and textures, even under high compression ratios, resulting in reconstructions that closely resemble the original images.These findings underscore the practical potential of the ETAM method for efficient, high-quality image compression in real-world applications such as transmission line monitoring under constrained network conditions.