mlsanityData to 7 Oct 2026

A study of deep perceptual metrics for image quality assessment

arXiv 2202.08692 ↗First posted 17 Feb 2022cs.CV cs.AI cs.GRcited by 5

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Abstract (arXiv)

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

Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). Builds on 22 of its 33 references that are in the map. First posted on arXiv 17 Feb 2022.

Builds on

22 of 33 references.

References · 22

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10 more
  1. 2016SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model sizecited by 1,054
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Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 2; 2024: 0; 2025: 2; 2026: 1.

5 papers.

  1. 2025ASCIIBench: Evaluating Language-Model-Based Understanding of Visually-Oriented Textcited by 4
  2. 2023Deep Perceptual Similarity is Adaptable to Ambiguous Contextscited by 0
  3. 2023Dreaming of Electrical Waves: Generative Modeling of Cardiac Excitation Waves using Diffusion Modelscited by 0
  4. 2025Hi-DREAM: Brain-Inspired Hierarchical Diffusion for fMRI-to-Image Reconstruction via ROI Encoder and VisuAl Mappingcited by 0
  5. 2026CAT-Flow: Curvature-Adaptive sTeps for Flow Matchingcited by 0