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Several metrics exist to quantify the similarity between images, but they areinefficient when it comes to measure the similarity of highly distorted images.In this work, we propose to empirically investigate perceptual metrics based ondeep neural networks for tackling the Image Quality Assessment (IQA) task. Westudy deep perceptual metrics according to different hyperparameters like thenetwork's architecture or training procedure. Finally, we propose ourmulti-resolution perceptual metric (MR-Perceptual), that allows us to aggregateperceptual information at different resolutions and outperforms standardperceptual metrics on IQA tasks with varying image deformations. Our code isavailable at https://github.com/ENSTA-U2IS/MR_perceptual

A study of deep perceptual metrics for image quality assessment