培训和测试结构相似度指标用于结构无损压缩
概括
我们开发了一种新方法来训练结构纹理相似度指标 (STSIMs) 用于图像压缩. 这种方法显著提高了性能,并为深度学习方法提供了计算效率高的替代方案.
科学领域:
- 计算机视觉 计算机视觉
- 图像处理 图像处理
- 数据压缩数据压缩
背景情况:
- 图像中的纹理冗余是有效压缩的关键挑战.
- 现有的结构纹理相似度指标 (STSIM) 需要强大的培训和测试方法.
- 自然纹理图像包含了需要考虑的特征性扰动.
研究的目的:
- 为培训和测试STSIMs.开发一个系统的方法.
- 为了利用纹理冗余来实现结构无损的图像压缩.
- 创建一个数据驱动的方法来训练STSIM的Mahalanobis配方.
主要方法:
- 一组模仿自然纹理干扰的图像扭曲用于训练和测试.
- 进行实证研究,以确定原始和扭曲纹理的感知相似度.
- 使用数据驱动的方法来训练STSIM的Mahalanobis配方,使用注释的纹理对.
主要成果:
- 培训STSIM带来了在指标性能上的显著改善.
- 经过训练的STSIM指标与基于最先进的卷积神经网络 (CNN) 的指标相比,表现出了竞争力.
- 拟议的方法比基于CNN的方法提供了比较低的计算成本.
结论:
- 一种系统和数据驱动的方法提高了用于图像压缩的STSIM的性能.
- 训练有素的STSIM为复杂的深度学习模型提供了高效和有效的替代方案,用于纹理分析.
- 这项研究有助于通过改进的纹理相似性评估来推进结构无损的图像压缩技术.
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