mlsanityData to 7 Oct 2026

VQualA 2025 Challenge on Image Super-Resolution Generated Content Quality Assessment: Methods and Results

arXiv 2509.06413 ↗First posted 8 Sep 2025cs.CV eess.IVcited by 5

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Abstract (arXiv)

This paper presents the ISRGC-Q Challenge, built upon the Image Super-Resolution Generated Content Quality Assessment (ISRGen-QA) dataset, and organized as part of the Visual Quality Assessment (VQualA) Competition at the ICCV 2025 Workshops. Unlike existing Super-Resolution Image Quality Assessment (SR-IQA) datasets, ISRGen-QA places a greater emphasis on SR images generated by the latest generative approaches, including Generative Adversarial Networks (GANs) and diffusion models. The primary goal of this challenge is to analyze the unique artifacts introduced by modern super-resolution techniques and to evaluate their perceptual quality effectively. A total of 108 participants registered for the challenge, with 4 teams submitting valid solutions and fact sheets for the final testing phase. These submissions demonstrated state-of-the-art (SOTA) performance on the ISRGen-QA dataset. The project is publicly available at: https://github.com/Lighting-YXLI/ISRGen-QA.

Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). Builds on 37 of its 54 references that are in the map. First posted on arXiv 8 Sep 2025.

Builds on

37 of 54 references.

References · 37

  1. 2016SGDR: Stochastic Gradient Descent with Warm Restartscited by 4,993
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25 more
  1. 2023SeeSR: Towards Semantics-Aware Real-World Image Super-Resolutioncited by 311
  2. 2024Scaling Up to Excellence: Practicing Model Scaling for Photo-Realistic Image Restoration In the Wildcited by 281
  3. 2023Blind Image Quality Assessment via Vision-Language Correspondence: A Multitask Learning Perspectivecited by 276
  4. 2023Implicit Diffusion Models for Continuous Super-Resolutioncited by 216
  5. 2019RankSRGAN: Generative Adversarial Networks with Ranker for Image Super-Resolutioncited by 145
  6. 2016Learning a No-Reference Quality Metric for Single-Image Super-Resolutioncited by 143
  7. 2020Uncertainty-Aware Blind Image Quality Assessment in the Laboratory and Wildcited by 134
  8. 2024Transcending the Limit of Local Window: Advanced Super-Resolution Transformer with Adaptive Token Dictionarycited by 63
  9. 2023Super-Resolution Neural Operatorcited by 48
  10. 2024CAMixerSR: Only Details Need More "Attention"cited by 30
  11. 2024SeD: Semantic-Aware Discriminator for Image Super-Resolutioncited by 26
  12. 2022Quality Assessment of Image Super-Resolution: Balancing Deterministic and Statistical Fidelitycited by 24
  13. 2025DifIISR: A Diffusion Model with Gradient Guidance for Infrared Image Super-Resolutioncited by 21
  14. 2020Blind Quality Assessment for Image Superresolution Using Deep Two-Stream Convolutional Networkscited by 15
  15. 2021Image Super-Resolution Quality Assessment: Structural Fidelity Versus Statistical Naturalnesscited by 15
  16. 2023Dynamic Attention-Guided Diffusion for Image Super-Resolutioncited by 10
  17. 2024SAM-DiffSR: Structure-Modulated Diffusion Model for Image Super-Resolutioncited by 10
  18. 2020Learning-Based Quality Assessment for Image Super-Resolutioncited by 9
  19. 2024CDFormer:When Degradation Prediction Embraces Diffusion Model for Blind Image Super-Resolutioncited by 8
  20. 2024Boosting Flow-based Generative Super-Resolution Models via Learned Priorcited by 7
  21. 2025VQualA 2025 Challenge on Engagement Prediction for Short Videos: Methods and Resultscited by 6
  22. 2024Diffusion Prior Interpolation for Flexibility Real-World Face Super-Resolutioncited by 5
  23. 2025Perceptual Visual Quality Assessment: Principles, Methods, and Future Directionscited by 5
  24. 2024Deep Bi-directional Attention Network for Image Super-Resolution Quality Assessmentcited by 4
  25. 2025VQualA 2025 Challenge on Face Image Quality Assessment: Methods and Resultscited by 4

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 1; 2026: 4.

5 papers.

  1. 2026How far have we gone in Generative Image Restoration? A study on its capability, limitations and evaluation practicescited by 2
  2. 2026Self-Supervised Image Super-Resolution Quality Assessment based on Content-Free Multi-Model Oriented Representation Learningcited by 1
  3. 2026Position: Evaluation of Visual Processing Should Be Human-Centered, Not Metric-Centeredcited by 1
  4. 2025VQualA 2025 Challenge on Visual Quality Comparison for Large Multimodal Models: Methods and Resultscited by 0
  5. 2026Perceptual Quality Optimization of Image Super-Resolutioncited by 0

Similar, not cited

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  1. 2025AU-IQA: A Benchmark Dataset for Perceptual Quality Assessment of AI-Enhanced User-Generated Content
  2. 2025ViDA-UGC: Detailed Image Quality Analysis via Visual Distortion Assessment for UGC Images
  3. 2024Enhancing Descriptive Image Quality Assessment with A Large-scale Multi-modal Dataset
  4. 2025NTIRE 2025 Challenge on Short-form UGC Video Quality Assessment and Enhancement: Methods and Results
  5. 2025Next Token Is Enough: Realistic Image Quality and Aesthetic Scoring with Multimodal Large Language Model
  6. 2025Toward Generalized Image Quality Assessment: Relaxing the Perfect Reference Quality Assumption
  7. 2024Q-Ground: Image Quality Grounding with Large Multi-modality Models
  8. 2026Learning Where to Look and How to Judge: Resolution-agnostic Image Quality Assessment with Quality-aware Saliency