82 citations · 187 across the 22 of their papers we have counts for
25 papers
Semantic-Aware Neural Video Codec for Error-Resilient Low-Latency Transmission
Matin Mortaheb, Homa Esfahanizadeh, Jinfeng Du +1
Emerging physical AI systems require low-latency, task-oriented video communication over unreliable channels. We propose a semantic-aware multi-level neural video coding method for…
UniTAC: Universal Task-Aware Compression via Weighted Distortion Measures
Homa Esfahanizadeh, Matin Mortaheb, Jinfeng Du +2
Lossy compression is conventionally driven by a task-agnostic distortion (e.g., MSE or MS-SSIM), yet in many emerging applications the receiver cares not about uniform fidelity but…
Towards Robust Semantic Video Transmission over Block Erasure Channels
Nargis Fayaz, Homa Esfahanizadeh, Matin Mortaheb +2
This paper investigates semantic-aware neural joint source-channel coding (JSCC) for robust video transmission over block erasure channels. We propose a neural video compression fr…
Lossy Joint Source-Channel Coding over Unknown Channels
Adeel Mahmood, Harish Viswanathan, Jinfeng Du
We analyze the performance of joint source-channel codes in an unknown-channel framework, where the true channel is unknown but the source distribution is known. We derive achievab…
Block Erasure-Aware Semantic Multimedia Compression via JSCC Autoencoder
Homa Esfahanizadeh, Nargis Fayaz, Jinfeng Du +1
We present an AI-based framework for semantic transmission of multimedia data over band-limited, time-varying channels. The method targets scenarios where large content is split in…
Demonstrating Interoperable Channel State Feedback Compression with Machine Learning
Dani Korpi, Rachel Wang, Jerry Wang +20
Neural network-based compression and decompression of channel state feedback has been one of the most widely studied applications of machine learning (ML) in wireless networks. Var…