mlsanityData to 14 Oct 2026

Can No-Reference Quality-Assessment Methods Serve as Perceptual Losses for Super-Resolution?

arXiv 2405.20392 ↗First posted 30 May 2024eess.IV cs.CVcited by 4

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Abstract (arXiv)

Perceptual losses play an important role in constructing deep-neural-network-based methods by increasing the naturalness and realism of processed images and videos. Use of perceptual losses is often limited to LPIPS, a fullreference method. Even though deep no-reference image-qualityassessment methods are excellent at predicting human judgment, little research has examined their incorporation in loss functions. This paper investigates direct optimization of several video-superresolution models using no-reference image-quality-assessment methods as perceptual losses. Our experimental results show that straightforward optimization of these methods produce artifacts, but a special training procedure can mitigate them.

Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Builds on 22 of its 28 references that are in the map. First posted on arXiv 30 May 2024.

Builds on

22 of 28 references.

References · 22

  1. 2018The Unreasonable Effectiveness of Deep Features as a Perceptual Metriccited by 11,177
  2. 2016Perceptual Losses for Real-Time Style Transfer and Super-Resolutioncited by 2,694
  3. 2022Exploring CLIP for Assessing the Look and Feel of Imagescited by 991
  4. 2017GradNorm: Gradient Normalization for Adaptive Loss Balancing in Deep Multitask Networkscited by 834
  5. 2020Image Quality Assessment: Unifying Structure and Texture Similaritycited by 811
  6. 2017Video Enhancement with Task-Oriented Flowcited by 619
  7. 2022MANIQA: Multi-dimension Attention Network for No-Reference Image Quality Assessmentcited by 522
  8. 2017Deep Laplacian Pyramid Networks for Fast and Accurate Super-Resolutioncited by 453
  9. 2016Optical Flow Estimation using a Spatial Pyramid Networkcited by 424
  10. 2017NIMA: Neural Image Assessmentcited by 389
  11. 2021BasicVSR++: Improving Video Super-Resolution with Enhanced Propagation and Alignmentcited by 342
  12. 2019Blind Image Quality Assessment Using A Deep Bilinear Convolutional Neural Networkcited by 302
10 more
  1. 2019From Patches to Pictures (PaQ-2-PiQ): Mapping the Perceptual Space of Picture Qualitycited by 223
  2. 2022VRT: A Video Restoration Transformercited by 220
  3. 2013Gradient Magnitude Similarity Deviation: A Highly Efficient Perceptual Image Quality Indexcited by 195
  4. 2018PieAPP: Perceptual Image-Error Assessment through Pairwise Preferencecited by 160
  5. 2020Unified Quality Assessment of In-the-Wild Videos with Mixed Datasets Trainingcited by 72
  6. 2022On the Generalization of BasicVSR++ to Video Deblurring and Denoisingcited by 19
  7. 2023Comparing the Robustness of Modern No-Reference Image- and Video-Quality Metrics to Adversarial Attackscited by 14
  8. 2023A Systematic Performance Analysis of Deep Perceptual Loss Networks: Breaking Transfer Learning Conventionscited by 11
  9. 2020iSeeBetter: Spatio-temporal video super-resolution using recurrent generative back-projection networkscited by 9
  10. 2023Perceptual impact of the loss function on deep-learning image coding performancecited by 2

Built on by

Cited by year (2026 to 14 Oct)

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

4 papers.

  1. 2025Cross-Modal Transferable Image-to-Video Attack on Video Quality Metricscited by 9
  2. 2024Guardians of Image Quality: Benchmarking Defenses Against Adversarial Attacks on Image Quality Metricscited by 5
  3. 2026BiRQA: Bidirectional Robust Quality Assessment for Imagescited by 1
  4. 2024Stochastic BIQA: Median Randomized Smoothing for Certified Blind Image Quality Assessmentcited by 0