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<p>In wireless communications, transmitting images efficiently requires a careful balance of robustness, throughput, and latency, particularly under dynamic channel conditions. This paper introduces an adaptive, progressive transmission pipeline for two state-of-the-art learned image compression (LIC) architectures: a hyperprior-based model and a Vector Quantized Generative Adversarial Network (VQGAN). We design and evaluate progressive versions of both models, enabling partial image reconstruction that gracefully adapts to fluctuating channel quality. Our evaluation, conducted over a simulated Rayleigh fading channel, focuses on the often-overlooked metric of transmission waiting time. Results show that our progressive framework significantly enhances robustness and reduces latency in low Signal-to-Noise Ratio (SNR) environments where standard codecs like adaptive WebP fail. Specifically, the progressive hyperprior model excels in minimizing latency, making it ideal for delay-sensitive applications. In contrast, the progressive VQGAN provides superior image quality in poor channel conditions without requiring channel coding. Our work demonstrates that tailoring LIC models with a progressive transmission strategy offers a robust and efficient solution for reliable image delivery in challenging wireless systems.</p>

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Deep learning-based image compression for wireless communications: impacts on robustness, throughput, and latency

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年4月2日

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  • Mostafa Naseri,
  • Pooya Ashtari,
  • Mohamed Seif,
  • Eli De Poorter,
  • H. Vincent Poor,
  • Adnan Shahid

摘要

In wireless communications, transmitting images efficiently requires a careful balance of robustness, throughput, and latency, particularly under dynamic channel conditions. This paper introduces an adaptive, progressive transmission pipeline for two state-of-the-art learned image compression (LIC) architectures: a hyperprior-based model and a Vector Quantized Generative Adversarial Network (VQGAN). We design and evaluate progressive versions of both models, enabling partial image reconstruction that gracefully adapts to fluctuating channel quality. Our evaluation, conducted over a simulated Rayleigh fading channel, focuses on the often-overlooked metric of transmission waiting time. Results show that our progressive framework significantly enhances robustness and reduces latency in low Signal-to-Noise Ratio (SNR) environments where standard codecs like adaptive WebP fail. Specifically, the progressive hyperprior model excels in minimizing latency, making it ideal for delay-sensitive applications. In contrast, the progressive VQGAN provides superior image quality in poor channel conditions without requiring channel coding. Our work demonstrates that tailoring LIC models with a progressive transmission strategy offers a robust and efficient solution for reliable image delivery in challenging wireless systems.