快速和高性能学习图像压缩与改进的棋盘上下文模型,可变形残余模块和知识蒸.
概括
本研究介绍了基于深度学习的更快,更有效的图像压缩的四种技术. 这些新方法显著加快了编码和解码的速度,同时提高了比现有方案的速率扭曲性能.
科学领域:
- 计算机视觉 计算机视觉
- 机器学习 机器学习
- 图像处理 图像处理
背景情况:
- 基于深度学习的图像压缩显示出希望,但受到缓慢的串行处理和高网络复杂性的困扰.
- 现有的方法往往难以平衡速率扭曲性能和计算效率.
研究的目的:
- 开发用于深度学习图像压缩的新技术,以提高速度和降低复杂性,而不会影响性能.
- 解决串行上下文适应性模型的局限性和当前学习图像编解码器的高计算需求.
主要方法:
- 引入了一个可变形的残余模块,以改善冗余清除.
- 设计了一个改进的棋盘上下文模型,允许并行解码.
- 实施了三通知识蒸方案,以减少解码器复杂性.
- 应用L1规范化用于稀疏的隐性表示,只编码非零通道.
主要成果:
- 实现的编码速度大约是最先进的学习方法的20倍,解码速度是70-90倍.
- 在速率扭曲性能方面表现出2.3%的改善.
- 超越了H.266/VVC-intra等经典编码器和使用PSNR和MS-SSIM的标准数据集 (Kodak,Tecnick-40) 上最近学到的方法.
结论:
- 提出的技术有效地平衡了深度学习图像压缩中的扭曲率性能和计算复杂性.
- 该方法在编码和解码方面都提供了显著的加速,使其适合实际应用.
- 这种方法为高效和高性能的学习图像编码设定了新的基准.
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