Block Erasure-Aware Semantic Multimedia Compression via JSCC Autoencoder

TL;DR

This paper introduces a JSCC autoencoder framework for semantic multimedia transmission that ensures reliable reconstruction over unreliable channels, reducing delays and improving robustness in latency-sensitive applications.

Contribution

It proposes a novel block erasure-aware semantic compression method using JSCC autoencoders, enhancing robustness and compatibility with existing protocols.

Findings

Significant robustness improvement over state-of-the-art methods.

Effective handling of packet drops in multimedia transmission.

Flexible design balancing robustness and fidelity.

Abstract

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 into multiple packets, with an unknown number potentially dropped due to channel impairments. Using joint source-channel coding (JSCC), our approach achieves reliable semantic reconstruction with graceful quality degradation as channel conditions worsen, eliminating the need for retransmissions that cause unacceptable delays in latency-sensitive applications such as video conferencing and robotic control. The framework is compatible with existing network protocols and further enables intelligent congestion control and unequal error protection. A tunable design parameter allows balancing robustness at low channel quality against fidelity at high channel quality. Experiments demonstrate significant robustness improvement over state-of-the-art baselines in both image and video domains.