arXiv:2606. 12858v1 Announce Type: cross Abstract: Conventional communication systems, including both separation-based coding and learning-based joint source-channel coding (JSCC), are typically designed under Shannon's rate-distortion theory.
By Tong Wu, Zhiyong Chen, Guo Lu, Li Song, Feng Yang, Meixia Tao, Wenjun Zhang
arXiv:2601. 10267v2 Announce Type: replace Abstract: Separate Source-Channel Coding (SSCC) remains attractive for text transmission due to its modularity and compatibility with mature entropy coders and powerful channel codes.
By Ziqiong Wang, Tianqi Ren, Rongpeng Li, Zhifeng Zhao, Honggang Zhang
The paper introduces a semantic‑aware multi‑level neural video codec designed for low‑latency, task‑oriented video transmission over unreliable channels. It builds on the real‑time DCVC‑RT codec by partitioning encoded representations into packets of varying semantic and feature importance, assigning them to priority streams, and employing an error‑resilient entropy model that removes inter‑packet dependencies. Experiments demonstrate that this framework improves robustness against packet erasures, achieving graceful degradation in less important regions while preserving task‑relevant visual content.
By Matin Mortaheb, Homa Esfahanizadeh, Jinfeng Du, Harish Viswanathan
arXiv:2609.20899v1 Announce Type: cross
Abstract: Satellite communications are an essential component of sixth-generation (6G) mobile networks, which provide ubiquitous connectivity for global servic...
By Jianhao Huang, Zhanwei Wang, Khaled B. Letaief, Kaibin Huang
This article reviews recent diffusion‑based methods for generative lossy image compression, highlighting how these techniques encode a source into an embedding and use a diffusion model to iteratively refine the reconstruction during decoding. It discusses the role of auxiliary entropy models for transmitting the embedding, explores the use of diffusion models for information transmission via channel simulation, and frames the discussion within rate‑distortion‑perception theory, common randomness, and inverse‑problem connections. The review also identifies open challenges in the field.
By Yibo Yang, Stephan Mandt
arXiv:2606. 00111v1 Announce Type: cross Abstract: State-of-the-art learned image compression (LIC) schemes are increasingly based on hybrid CNN-transformer architectures.
By Haisheng Fu, Runyu Yang, Feng Ding, Siyu Zhu, Jie Liang, Xiaoxiao Li, Zhenman Fang, Jingning Han