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揭示人类和机器编码的未来:对端到端学习图像压缩的调查
1Department of Computer Science and Information Engineering, National Taiwan University, Taipei 106, Taiwan.
Entropy (Basel, Switzerland)
|May 24, 2024
概括
学习的图像压缩方法提供了卓越的性能和灵活性. 本综述涵盖了这些先进技术,并突出了MPEG视频编码用于机器和JPEGAI用于计算机视觉集成等标准.
科学领域:
- 计算机视觉 计算机视觉
- 机器学习 机器学习
- 信号处理 信号处理
背景情况:
- 端到端学习的图像压缩编解码正在成为传统方法的优越替代方案.
- 这些编解码器在各种数据中表现出灵活性和适应性,支持新的扭曲指标.
- 学习的压缩方法与数据处理管道很好地结合在一起,因为它们的内部表示.
研究的目的:
- 提供对损失图像压缩方法的全面调查和审查.
- 提供国际标准的概述,如MPEG视频编码机器和JPEGAI.
- 探索数据压缩与计算机视觉应用程序的整合.
主要方法:
- 综述了端到端学习图像压缩的最新进展.
- 分析学习的编解码器的性能,灵活性和适应性.
- 关于机器视觉编码的既定和新兴标准的概述.
主要成果:
- 学习图像压缩比传统方法具有显著的优势.
- 机器视频编码标准对于弥合压缩和计算机视觉至关重要.
- 学习式编解码器的内部表示非常适合数据驱动的管道.
结论:
- 学习图像压缩是一个快速发展的领域,具有巨大的潜力.
- 像机器MPEG视频编码和JPEGAI这样的标准是实际应用的关键.
- 压缩和计算机视觉的整合对于未来的数据处理至关重要.
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