This paper reports on the NTIRE 2021 challenge on perceptual image quality assessment (IQA), held in conjunction with the New Trends in Image Restoration and Enhancement workshop (NTIRE) workshop at CVPR 2021. As a new type of image processing technology, perceptual image processing algorithms based on Generative Adversarial Networks (GAN) have produced images with more realistic textures. These output images have completely different characteristics from traditional distortions, thus pose a new challenge for IQA methods to evaluate their visual quality. In comparison with previous IQA challenges, the training and testing datasets in this challenge include the outputs of perceptual image processing algorithms and the corresponding subjective scores. Thus they can be used to develop and evaluate IQA methods on GAN-based distortions. The challenge has 270 registered participants in total. In the final testing stage, 13 participating teams submitted their models and fact sheets. Almost all of them have achieved much better results than existing IQA methods, while the winning method can demonstrate state-of-the-art performance.
翻译:本文报告了2021年NTRE关于视觉图像质量评估的挑战,与2021年CVPR的图像恢复与增强新趋势讲习班一起举行。作为一种新型图像处理技术,基于General Aversarial Networks(GAN)的视觉图像处理算法产生了更加现实的图象,这些产出图象具有与传统扭曲完全不同的特点,因此对IQA评估其视觉质量的方法构成新的挑战。与以前IQA的挑战相比,这项挑战的培训和测试数据集包括视觉图像处理算法的产出和相应的主观分数。因此,它们可用于开发和评估基于GAN的图像处理方法,共有270名注册参与者。在最后测试阶段,13个参与小组提交了模型和事实表,几乎所有这些小组都取得了比现有的IQA方法更好的结果,而获胜的方法可以展示最新业绩。