在基于升降的图像编码器中,共同学习完全连接的网络模型.
概括
本研究介绍了一种新的多尺度优化技术,用于在基于提升的图像编码中共同学习完全连接的神经网络 (FCNN) 模型. 这种方法提高了带损耗和无损耗的图像压缩性能.
科学领域:
- 计算机视觉 计算机视觉
- 图像处理 图像处理
- 机器学习 机器学习
背景情况:
- 基于提升的图像编码方案严重依赖于优化预测和更新操作员.
- 完全连接的神经网络 (FCNNs) 为这些起重结构提供了近期的进步.
研究的目的:
- 开发一种联合学习基于FCNN的预测和升降结构更新模型的方法.
- 为了提高图像压缩算法的效率和有效性.
主要方法:
- 开发了一个基于统计模型的损失函数,以近似编码速率.
- 引入了一种多尺度优化技术,同时学习所有FCNN模型.
- 在不同的分辨率级别中研究了两个不同的损失函数:一个结合了标准损失,另一个接近了速率扭曲标准.
主要成果:
- 拟议的多尺度优化技术有效地同时学习FCNN模型.
- 实验结果表明,对有损和无损的图像压缩都有显著的好处.
- 开发的损失函数为编码速率和速率扭曲提供了良好的近似值.
结论:
- 使用多尺度优化共同学习FCNN模型比单独学习更有效.
- 拟议的方法为先进的图像压缩技术提供了一个有希望的方向.
- 该方法显示了对各种压缩场景的标准图像数据集的实际优势.
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