# Learning-Based Quality Assessment for Image Super-Resolution

arXiv: https://arxiv.org/abs/2012.08732 · HTML: https://mlsanity.com/paper/2012.08732/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Dec 2020.

## Built on by (most cited first, 9 of 9)

- [VQualA 2025 Challenge on Image Super-Resolution Generated Content Quality Assessment: Methods and Results](https://mlsanity.com/paper/2509.06413/) (2025)
- [Scale Guided Hypernetwork for Blind Super-Resolution Image Quality Assessment](https://mlsanity.com/paper/2306.02398/) (2023)
- [Perception- and Fidelity-aware Reduced-Reference Super-Resolution Image Quality Assessment](https://mlsanity.com/paper/2405.09472/) (2024)
- [SPQE: Structure-and-Perception-Based Quality Evaluation for Image Super-Resolution](https://mlsanity.com/paper/2205.03584/) (2022)
- [Deep Quality Assessment of Compressed Videos: A Subjective and Objective Study](https://mlsanity.com/paper/2205.03630/) (2022)
- [Perception-oriented Bidirectional Attention Network for Image Super-resolution Quality Assessment](https://mlsanity.com/paper/2509.06442/) (2025)
- [Exploring the Effect of Sparse Recovery on the Quality of Image Superresolution](https://mlsanity.com/paper/2308.02714/) (2023)
- [Self-Supervised Image Super-Resolution Quality Assessment based on Content-Free Multi-Model Oriented Representation Learning](https://mlsanity.com/paper/2602.10744/) (2026)
- [PixelBoost: Leveraging Brownian Motion for Realistic-Image Super-Resolution](https://mlsanity.com/paper/2506.23254/) (2025)

## Abstract (arXiv)

Image Super-Resolution (SR) techniques improve visual quality by enhancing the spatial resolution of images. Quality evaluation metrics play a critical role in comparing and optimizing SR algorithms, but current metrics achieve only limited success, largely due to the lack of large-scale quality databases, which are essential for learning accurate and robust SR quality metrics. In this work, we first build a large-scale SR image database using a novel semi-automatic labeling approach, which allows us to label a large number of images with manageable human workload. The resulting SR Image quality database with Semi-Automatic Ratings (SISAR), so far the largest of SR-IQA database, contains 8,400 images of 100 natural scenes. We train an end-to-end Deep Image SR Quality (DISQ) model by employing two-stream Deep Neural Networks (DNNs) for feature extraction, followed by a feature fusion network for quality prediction. Experimental results demonstrate that the proposed method outperforms state-of-the-art metrics and achieves promising generalization performance in cross-database tests. The SISAR database and DISQ model will be made publicly available to facilitate reproducible research.