In wireless communications, efficient image transmission must balance reliability, throughput, and latency, especially under dynamic channel conditions. This paper presents an adaptive and progressive pipeline for learned image compression (LIC)-based architectures tailored to such environments. We investigate two state-of-the-art learning-based models: the hyperprior model and Vector Quantized Generative Adversarial Network (VQGAN). The hyperprior model achieves superior compression performance through lossless compression in the bottleneck but is susceptible to bit errors, necessitating the use of error correction or retransmission mechanisms. In contrast, the VQGAN decoder demonstrates robust image reconstruction capabilities even in the absence of channel coding, enhancing reliability in challenging transmission scenarios. We propose progressive versions of both models, enabling partial image transmission and decoding under imperfect channel conditions. This progressive approach not only maintains image integrity under poor channel conditions but also significantly reduces latency by allowing immediate partial image availability. We evaluate our pipeline using the Kodak high-resolution image dataset under a Rayleigh fading wireless channel model simulating dynamic conditions. The results indicate that the progressive transmission framework enhances reliability and latency while maintaining or improving throughput compared to non-progressive counterparts across various Signal-to-Noise Ratio (SNR) levels. Specifically, the progressive-hyperprior model consistently outperforms others in latency metrics, particularly in the 99.9th percentile waiting time-a measure indicating the maximum waiting time experienced by 99.9% of transmission instances-across all SNRs, and achieves higher throughput in low SNR scenarios. where Adaptive WebP fails.

翻译：在无线通信中，高效的图像传输必须在可靠性、吞吐量和延迟之间取得平衡，尤其是在动态信道条件下。本文提出了一种适用于此类环境的、基于学习型图像压缩（LIC）架构的自适应渐进式传输流水线。我们研究了两种先进的基于学习的模型：超先验模型和矢量量化生成对抗网络（VQGAN）。超先验模型通过在瓶颈处进行无损压缩实现了卓越的压缩性能，但易受比特错误影响，因此需要使用纠错或重传机制。相比之下，VQGAN解码器即使在缺乏信道编码的情况下也表现出强大的图像重建能力，从而提升了在挑战性传输场景下的可靠性。我们提出了两种模型的渐进式版本，使得在不完美的信道条件下能够进行部分图像传输和解码。这种渐进式方法不仅能在恶劣信道条件下保持图像完整性，还能通过允许即时获取部分图像显著降低延迟。我们在模拟动态条件的瑞利衰落无线信道模型下，使用Kodak高分辨率图像数据集评估了我们的流水线。结果表明，与非渐进式方案相比，在各种信噪比（SNR）水平下，渐进式传输框架在保持或提升吞吐量的同时，增强了可靠性和降低了延迟。具体而言，渐进式超先验模型在所有SNR下的延迟指标（特别是99.9%等待时间——该指标表示99.9%的传输实例所经历的最大等待时间）上持续优于其他模型，并在低SNR场景下实现了更高的吞吐量，而自适应WebP在此类场景中则表现不佳。