论文标题
Pytorch图像质量：图像质量评估的指标
PyTorch Image Quality: Metrics for Image Quality Assessment
论文作者
论文摘要
图像质量评估（IQA）指标被广泛用于定量估计某些形成，恢复，转化或增强算法后图像降解的程度。我们提出了Pytorch图像质量（PIQ），这是一个以可用性为中心的库，其中包含最受欢迎的现代IQA算法，并保证根据其原始命题正确实现并进行了彻底验证。在本文中，我们详细介绍了图书馆基础背后的原理，描述了使其可靠的评估策略，提供了展示性能时间权衡的基准，并强调了GPU加速的好处，因为该图书馆在Pytorch后端使用了图书馆。 PyTorch Image Quality is an open source software: https://github.com/photosynthesis-team/piq/.
Image Quality Assessment (IQA) metrics are widely used to quantitatively estimate the extent of image degradation following some forming, restoring, transforming, or enhancing algorithms. We present PyTorch Image Quality (PIQ), a usability-centric library that contains the most popular modern IQA algorithms, guaranteed to be correctly implemented according to their original propositions and thoroughly verified. In this paper, we detail the principles behind the foundation of the library, describe the evaluation strategy that makes it reliable, provide the benchmarks that showcase the performance-time trade-offs, and underline the benefits of GPU acceleration given the library is used within the PyTorch backend. PyTorch Image Quality is an open source software: https://github.com/photosynthesis-team/piq/.
