# Deep Joint Source-Channel Coding for Wireless Image Retrieval

arXiv: https://arxiv.org/abs/1910.12703 · HTML: https://mlsanity.com/paper/1910.12703/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Oct 2019.

## Built on by (most cited first, 11 of 11)

- [Distributed Learning in Wireless Networks: Recent Progress and Future Challenges](https://mlsanity.com/paper/2104.02151/) (2021)
- [Learning Task-Oriented Communication for Edge Inference: An Information Bottleneck Approach](https://mlsanity.com/paper/2102.04170/) (2021)
- [Semantics-Empowered Communication: A Tutorial-cum-Survey](https://mlsanity.com/paper/2212.08487/) (2022)
- [Deep Joint Source-Channel Coding for Multi-Task Network](https://mlsanity.com/paper/2109.05779/) (2021)
- [Semantic Edge Computing and Semantic Communications in 6G Networks: A Unifying Survey and Research Challenges](https://mlsanity.com/paper/2411.18199/) (2024)
- [GeNet: A Graph Neural Network-based Anti-noise Task-Oriented Semantic Communication Paradigm](https://mlsanity.com/paper/2403.18296/) (2024)
- [Diffusion-aided Task-oriented Semantic Communications with Model Inversion Attack](https://mlsanity.com/paper/2506.19886/) (2025)
- [PACE: A Pragmatic Agent for Enhancing Communication Efficiency Using Large Language Models](https://mlsanity.com/paper/2402.01750/) (2024)
- [SNR and Resource Adaptive Deep JSCC for Distributed IoT Image Classification](https://mlsanity.com/paper/2506.10699/) (2025)
- [Stabilizing Information Flow Entropy: Regularization for Safe and Interpretable Autonomous Driving Perception](https://mlsanity.com/paper/2509.16277/) (2025)
- [Deep Joint Source-Channel Coding for Wireless Video Transmission with Asymmetric Context](https://mlsanity.com/paper/2601.06170/) (2026)

## Abstract (arXiv)

Motivated by surveillance applications with wireless cameras or drones, we consider the problem of image retrieval over a wireless channel. Conventional systems apply lossy compression on query images to reduce the data that must be transmitted over the bandwidth and power limited wireless link. We first note that reconstructing the original image is not needed for retrieval tasks; hence, we introduce a deep neutral network (DNN) based compression scheme targeting the retrieval task. Then, we completely remove the compression step, and propose another DNN-based communication scheme that directly maps the feature vectors to channel inputs. This joint source-channel coding (JSCC) approach not only improves the end-to-end accuracy, but also simplifies and speeds up the encoding operation which is highly beneficial for power and latency constrained IoT applications.