Deep Joint Source-Channel Coding for Wireless Image Retrieval

TL;DR

This paper introduces a deep joint source-channel coding approach for wireless image retrieval that enhances accuracy and efficiency by directly transmitting feature vectors, bypassing traditional image reconstruction.

Contribution

It proposes novel DNN-based schemes for image retrieval over wireless channels, including a direct feature-to-channel mapping method that improves performance and reduces latency.

Findings

Enhanced retrieval accuracy over wireless channels

Simplified encoding process for IoT devices

Faster transmission with reduced power consumption

Abstract

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.