图像质量评估:通过深度特征空间的分布测量来测量感知退化
概括
新的深度学习模型提供无培训的全参考图像质量评估 (FR-IQA),通过比较网络特征,使用像瓦瑟斯坦距离 (WSD) 这样的分布措施. 这些模型与人类的感知保持一致,以更好地评估图像质量.
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:
- 深度神经网络 (DNN) 越来越多地用于图像质量评估 (IQA).
- 在没有广泛的培训的情况下,开发有效的全参考IQA (FR-IQA) 模型,与人类的感知保持一致是具有挑战性的.
- 以感知方式比较深度网络特征对于推进IQA模型至关重要.
研究的目的:
- 开发使用深度神经网络的先进,无培训的FR-IQA模型.
- 调查分布措施,以感知地比较深度网络特征.
- 探索这些措施在图像增强任务中的使用.
主要方法:
- 使用的分布尺度:瓦瑟斯坦距离 (WSD),詹森-香农分歧 (JSD) 和对称库尔巴克-莱布勒分歧 (SKLD).
- 从预先训练的网络 (VGG,SqueezeNet,MobileNet,EfficientNet) 的深度特征进行比较.
- 在不同的IQA数据集上评估模型,以与主观的人类评估保持一致.
主要成果:
- 拟议的FR-IQA模型在各种数据集中与人类主观评估的优异对齐.
- 在比较深度网络特征时,分布测量显示了高级感知意识.
- 这些模型在不需要任何培训的情况下实现了这一目标,突出了它们的效率.
结论:
- 深度分发措施为FR-IQA提供了一个强大的,无培训的方法.
- 这些措施有效地捕捉了深度网络特征中的感知相关性.
- 这些发现表明,在引导感知图像增强方面,如超分辨率等方面,有潜在的应用.
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