Deep Joint Source-Channel Coding for Wireless Image Retrieval

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.

References in corpus (5)

• In Defense of the Triplet Loss for Person Re-Identification
• Deep Joint Source-Channel Coding for Wireless Image Transmission
• Variational image compression with a scale hyperprior
• Learning Discriminative Features with Multiple Granularities for Person Re-Identification
• Towards Image Understanding from Deep Compression without Decoding

Cited by in corpus (8)

• Edge Learning for B5G Networks with Distributed Signal Processing: Semantic Communication, Edge Computing, and Wireless Sensing
• Wireless Image Transmission Using Deep Source Channel Coding With Attention Modules
• Joint Device-Edge Inference over Wireless Links with Pruning
• Constellation Design for Deep Joint Source-Channel Coding
• SemHARQ: Semantic-Aware Hybrid Automatic Repeat Request for Multi-Task Semantic Communications
• Learning Task-Oriented Communication for Edge Inference: An Information Bottleneck Approach
• Deep Joint Source-Channel Coding for Multi-Task Network
• Deep Joint Transmission-Recognition for Multi-View Cameras