论文标题
对图像质量评估的深入感知指标的研究
A study of deep perceptual metrics for image quality assessment
论文作者
论文摘要
存在几个指标来量化图像之间的相似性，但是在测量高度扭曲的图像的相似性时，它们效率低下。在这项工作中，我们建议基于深层神经网络的经验研究以应对图像质量评估（IQA）任务的感知指标。我们根据网络的体系结构或培训程序等不同的超参数研究深度感知指标。最后，我们提出了多分辨率感知度量（MR感知），这使我们能够在不同分辨率上汇总感知信息，并且在具有不同图像变形的IQA任务上超过了标准的感知指标。我们的代码可从https://github.com/ensta-u2is/mr_perceptual获得
Several metrics exist to quantify the similarity between images, but they are inefficient when it comes to measure the similarity of highly distorted images. In this work, we propose to empirically investigate perceptual metrics based on deep neural networks for tackling the Image Quality Assessment (IQA) task. We study deep perceptual metrics according to different hyperparameters like the network's architecture or training procedure. Finally, we propose our multi-resolution perceptual metric (MR-Perceptual), that allows us to aggregate perceptual information at different resolutions and outperforms standard perceptual metrics on IQA tasks with varying image deformations. Our code is available at https://github.com/ENSTA-U2IS/MR_perceptual
