Artificial Intelligence Generated Content (AIGC) is leading a new technical
revolution for the acquisition of digital content and impelling the progress of
visual compression towards competitive performance gains and diverse
functionalities over traditional codecs. This paper provides a thorough review
on the recent advances of generative visual compression, illustrating great
potentials and promising applications in ultra-low bitrate communication,
user-specified reconstruction/filtering, and intelligent machine analysis. In
particular, we review the visual data compression methodologies with deep
generative models, and summarize how compact representation and high-fidelity
reconstruction could be actualized via generative techniques. In addition, we
generalize related generative compression technologies for machine vision and
intelligent analytics. Finally, we discuss the fundamental challenges on
generative visual compression techniques and envision their future research
directions.

人工智能生成内容（AIGC）已经引领了一场新的技术革命，推动了数字内容的获取并推动传统编解码器在性能提升和多样化功能方面的进展，本文全面回顾了生成式视觉压缩的最新进展，展示了在超低码率通信，用户指定的重建 / 滤波和智能机器分析方面的巨大潜力和有前景的应用。特别是，我们回顾了基于深度生成模型的视觉数据压缩方法，并总结了如何通过生成技术实现紧凑的表示和高保真重建。此外，我们概括了相关的生成式压缩技术用于机器视觉和智能分析。最后，我们讨论了生成式视觉压缩技术面临的基本挑战，并展望了它们未来的研究方向。