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

TL;DR

This paper introduces an adaptive, progressive image compression pipeline using learned models for wireless communications, improving reliability, latency, and throughput under dynamic channel conditions.

Contribution

It proposes novel progressive versions of hyperprior and VQGAN models tailored for wireless channels, enhancing robustness and efficiency in image transmission.

Findings

Progressive hyperprior outperforms others in latency, especially at high percentiles.

VQGAN demonstrates robustness without channel coding, improving reliability.

Framework maintains or improves throughput under various SNR conditions.

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.