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

arXiv: https://arxiv.org/abs/2411.10650 · HTML: https://mlsanity.com/paper/2411.10650/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 37 references that are in the map.
- First posted on arXiv 16 Nov 2024.

## Builds on (most cited first, 17 of 17)

- [Taming Transformers for High-Resolution Image Synthesis](https://mlsanity.com/paper/2012.09841/) (2020)
- [Variational image compression with a scale hyperprior](https://mlsanity.com/paper/1802.01436/) (2018)
- [Learned Image Compression with Discretized Gaussian Mixture Likelihoods and Attention Modules](https://mlsanity.com/paper/2001.01568/) (2020)
- [The Faiss library](https://mlsanity.com/paper/2401.08281/) (2024)
- [Generative Adversarial Networks for Extreme Learned Image Compression](https://mlsanity.com/paper/1804.02958/) (2018)
- [Learned Image Compression with Mixed Transformer-CNN Architectures](https://mlsanity.com/paper/2303.14978/) (2023)
- [Conditional Probability Models for Deep Image Compression](https://mlsanity.com/paper/1801.04260/) (2018)
- [Towards Flexible Blind JPEG Artifacts Removal](https://mlsanity.com/paper/2109.14573/) (2021)
- [Scaling the Codebook Size of VQGAN to 100,000 with a Utilization Rate of 99%](https://mlsanity.com/paper/2406.11837/) (2024)
- [SwinJSCC: Taming Swin Transformer for Deep Joint Source-Channel Coding](https://mlsanity.com/paper/2308.09361/) (2023)
- [Generative AI-driven Semantic Communication Networks: Architecture, Technologies and Applications](https://mlsanity.com/paper/2401.00124/) (2023)
- [Extreme Image Compression using Fine-tuned VQGANs](https://mlsanity.com/paper/2307.08265/) (2023)
- [Transformer-aided Wireless Image Transmission with Channel Feedback](https://mlsanity.com/paper/2306.09101/) (2023)
- [ProgDTD: Progressive Learned Image Compression with Double-Tail-Drop Training](https://mlsanity.com/paper/2305.02145/) (2023)
- [Layered Image Compression using Scalable Auto-encoder](https://mlsanity.com/paper/1904.00553/) (2019)
- [Vector Quantisation for Robust Segmentation](https://mlsanity.com/paper/2207.01919/) (2022)
- [Exploiting Inter-Image Similarity Prior for Low-Bitrate Remote Sensing Image Compression](https://mlsanity.com/paper/2407.12295/) (2024)

## Built on by (most cited first, 2 of 2)

- [Semantic-Aware Visual Information Transmission With Key Information Extraction Over Wireless Networks](https://mlsanity.com/paper/2506.12786/) (2025)
- [On the Robustness of Diffusion-Based Image Compression to Bit-Flip Errors](https://mlsanity.com/paper/2604.05743/) (2026)

## Abstract (arXiv)

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.