Deep Learning-Based Image Compression for Wireless Communications: Impacts on Reliability,Throughput, and Latency

TL;DR

An adaptive and progressive pipeline for learned image compression (LIC)-based architectures tailored to such environments is presented, indicating 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 levels.

Abstract

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.

Figures (5)

• Figure 1: System model for transmission based on the hyperprior model, illustrating the retention of $k\%$ of feature maps while masking the remainder in $Y$ and $Z$ at the transmitter, and the reconstruction by padding zeroes in the masked $100 - k\%$ feature maps at the receiver. The selection of $k$ is based on channel conditions and service requirements.
• Figure 2: System model for -based image transmission, illustrating the generation of feature maps ($Y$) and token maps ($Z$) during the encoding and decoding process.
• Figure 3: PSNR degradation versus the percentage of masked bottleneck channels for the image kodim05. The solid line represents unsorted masking, while the dashed line corresponds to observer-based feature masking.
• Figure 4: A 300 ms snapshot of the fading channel magnitude $|h|$, PSNR of the transmitted images for the three models, and waiting time $T$ of the image transmission system for progressive-hyperprior, progressive VQGAN, and adaptive WebP models over the snapshot.
• Figure 5: Visualization of $T_{avg}$ (ms) and performance metrics for the hyperprior model: (a) PSNR (dB) and (b) SSIM. The size of the circles represents throughput, with annotations on the circles indicating the channel SNR. By adjusting $N_{\text{max}}$, which determines the number of feature maps to be transmitted for each image, we can observe the impact on PSNR and SSIM, reflecting the tradeoff between reliability, latency, and throughput in varying channel conditions.