剖析深度特征作为感知图像质量指标的有效性
Pablo Hernández-Cámara1, Jorge Vila-Tomás1, Valero Laparra1
1Image Processing Lab., Universitat de València, 46980 Paterna, Spain.
概括
人工神经网络显示出类似人类的图像扭曲感知. 功能,架构和训练数据等因素显著影响这种相关性,这表明需要采用多方面的方法来理解人工智能.
科学领域:
- 计算神经科学是一种计算神经科学.
- 计算机视觉 计算机视觉 计算机视觉
- 人工智能的人工智能是人工智能.
背景情况:
- 人工神经网络 (ANN) 显示出类似人类的图像处理能力.
- 观察到ANN内部表示与人类对图像扭曲的感知之间的相关性,但尚未完全理解.
- 像结构相似性指数测量 (SSIM) 这样的现有指标在捕捉人类视觉质量感知方面存在局限性.
研究的目的:
- 调查导致ANN中类似人类行为的因素,用于图像扭曲评估.
- 剖析功能,架构和训练环境在塑造ANN-人类相关性中的作用.
- 将各种预训练模型的性能与人类感知和既定指标进行比较.
主要方法:
- 在不同的网络深度中评估了46个预训练的ANN配置.
- 在没有心理视觉训练数据的情况下,评估了ANN内部特征和人类扭曲感知之间的相关性.
- 将ANN性能与SSIM,LPIPS和DISTS指标进行比较.
主要成果:
- 大多数ANN与人类知觉的相关性比SSIM更高,其中一些超越了最先进的模型 (LPIPS,DISTS).
- 监督的分类任务比自我或非监督的任务产生了更好的人类相关性,尽管任务性能与人类相似性没有直接相关.
- 简单的架构和早期的网络层通常显示出更高的相关性;大,自然的数据集改善了相关性.
- 最佳的分类模型不一定会产生最好的人类距离预测.
结论:
- 在ANN中,类似人类的视觉感知受到任务功能,网络架构和训练环境的组合的影响.
- 通过ANN来理解人类视觉需要考虑多个层次的抽象,而不是单一的因素.
- 研究结果表明,ANN可以作为了解人类视觉系统的宝贵工具,只要承认它们的局限性和影响因素.
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