# Quality Assessment of Image Super-Resolution: Balancing Deterministic and Statistical Fidelity

arXiv: https://arxiv.org/abs/2207.08689 · HTML: https://mlsanity.com/paper/2207.08689/

## Key facts

- Cited by 24 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 50 references that are in the map.
- First posted on arXiv 15 Jul 2022.

## Builds on (most cited first, 12 of 12)

- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network](https://mlsanity.com/paper/1609.04802/) (2016)
- [Accurate Image Super-Resolution Using Very Deep Convolutional Networks](https://mlsanity.com/paper/1511.04587/) (2015)
- [Deep Laplacian Pyramid Networks for Fast and Accurate Super-Resolution](https://mlsanity.com/paper/1704.03915/) (2017)
- [Deep Learning for Image Super-resolution: A Survey](https://mlsanity.com/paper/1902.06068/) (2019)
- [From Patches to Pictures (PaQ-2-PiQ): Mapping the Perceptual Space of Picture Quality](https://mlsanity.com/paper/1912.10088/) (2019)
- [Gradient Magnitude Similarity Deviation: A Highly Efficient Perceptual Image Quality Index](https://mlsanity.com/paper/1308.3052/) (2013)
- [Learning a No-Reference Quality Metric for Single-Image Super-Resolution](https://mlsanity.com/paper/1612.05890/) (2016)
- [dipIQ: Blind Image Quality Assessment by Learning-to-Rank Discriminable Image Pairs](https://mlsanity.com/paper/1904.06505/) (2019)
- [Fast and Accurate Image Super Resolution by Deep CNN with Skip Connection and Network in Network](https://mlsanity.com/paper/1707.05425/) (2017)
- [Image Super-Resolution Quality Assessment: Structural Fidelity Versus Statistical Naturalness](https://mlsanity.com/paper/2105.07139/) (2021)
- [Encoding Visual Sensitivity by MaxPol Convolution Filters for Image Sharpness Assessment](https://mlsanity.com/paper/1808.00617/) (2018)

## Built on by (most cited first, 24 of 24)

- [Teaching Large Language Models to Regress Accurate Image Quality Scores using Score Distribution](https://mlsanity.com/paper/2501.11561/) (2025)
- [Depicting Beyond Scores: Advancing Image Quality Assessment through Multi-modal Language Models](https://mlsanity.com/paper/2312.08962/) (2023)
- [Enhancing Descriptive Image Quality Assessment with A Large-scale Multi-modal Dataset](https://mlsanity.com/paper/2405.18842/) (2024)
- [Toward Generalized Image Quality Assessment: Relaxing the Perfect Reference Quality Assumption](https://mlsanity.com/paper/2503.11221/) (2025)
- [Networking Systems for Video Anomaly Detection: A Tutorial and Survey](https://mlsanity.com/paper/2405.10347/) (2024)
- [Dog-IQA: Standard-guided Zero-shot MLLM for Mix-grained Image Quality Assessment](https://mlsanity.com/paper/2410.02505/) (2024)
- [Revisiting MLLM Based Image Quality Assessment: Errors and Remedy](https://mlsanity.com/paper/2511.07812/) (2025)
- [Scene Prior Filtering for Depth Super-Resolution](https://mlsanity.com/paper/2402.13876/) (2024)
- [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)
- [Bridging the Perception Gap in Image Super-Resolution Evaluation](https://mlsanity.com/paper/2503.13074/) (2025)
- [Beyond Score Changes: Adversarial Attack on No-Reference Image Quality Assessment from Two Perspectives](https://mlsanity.com/paper/2404.13277/) (2024)
- [CTSR: Controllable Fidelity-Realness Trade-off Distillation for Real-World Image Super Resolution](https://mlsanity.com/paper/2503.14272/) (2025)
- [Perception-oriented Bidirectional Attention Network for Image Super-resolution Quality Assessment](https://mlsanity.com/paper/2509.06442/) (2025)
- [F2IDiff: Real-world Image Super-resolution using Feature to Image Diffusion Foundation Model](https://mlsanity.com/paper/2512.24473/) (2025)
- [How far have we gone in Generative Image Restoration? A study on its capability, limitations and evaluation practices](https://mlsanity.com/paper/2603.05010/) (2026)
- [QMRNet: Quality Metric Regression for EO Image Quality Assessment and Super-Resolution](https://mlsanity.com/paper/2210.06618/) (2022)
- [Exploring the Effect of Sparse Recovery on the Quality of Image Superresolution](https://mlsanity.com/paper/2308.02714/) (2023)
- [Scene Perceived Image Perceptual Score (SPIPS): combining global and local perception for image quality assessment](https://mlsanity.com/paper/2504.17234/) (2025)
- [Self-Supervised Image Super-Resolution Quality Assessment based on Content-Free Multi-Model Oriented Representation Learning](https://mlsanity.com/paper/2602.10744/) (2026)
- [Temporal Inconsistency Guidance for Super-resolution Video Quality Assessment](https://mlsanity.com/paper/2412.18933/) (2024)
- [Enhancing Image Aesthetics with Dual-Conditioned Diffusion Models Guided by Multimodal Perception](https://mlsanity.com/paper/2603.11556/) (2026)
- [MSIQ: Moment-based Scale-Invariant Quality Measure for Single Image Super-Resolution](https://mlsanity.com/paper/2605.17588/) (2026)

## Abstract (arXiv)

There has been a growing interest in developing image super-resolution (SR) algorithms that convert low-resolution (LR) to higher resolution images, but automatically evaluating the visual quality of super-resolved images remains a challenging problem. Here we look at the problem of SR image quality assessment (SR IQA) in a two-dimensional (2D) space of deterministic fidelity (DF) versus statistical fidelity (SF). This allows us to better understand the advantages and disadvantages of existing SR algorithms, which produce images at different clusters in the 2D space of (DF, SF). Specifically, we observe an interesting trend from more traditional SR algorithms that are typically inclined to optimize for DF while losing SF, to more recent generative adversarial network (GAN) based approaches that by contrast exhibit strong advantages in achieving high SF but sometimes appear weak at maintaining DF. Furthermore, we propose an uncertainty weighting scheme based on content-dependent sharpness and texture assessment that merges the two fidelity measures into an overall quality prediction named the Super Resolution Image Fidelity (SRIF) index, which demonstrates superior performance against state-of-the-art IQA models when tested on subject-rated datasets.