# Boosting Image Quality Assessment Performance: Unsupervised Score Fusion by Deep Maximum a Posteriori Estimation

arXiv: https://arxiv.org/abs/2605.30269 · HTML: https://mlsanity.com/paper/2605.30269/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 43 references that are in the map.
- First posted on arXiv 28 May 2026.

## Builds on (most cited first, 2 of 2)

- [Gradient Magnitude Similarity Deviation: A Highly Efficient Perceptual Image Quality Index](https://mlsanity.com/paper/1308.3052/) (2013)
- [Quantifying Visual Image Quality: A Bayesian View](https://mlsanity.com/paper/2102.00195/) (2021)

## Built on by (most cited first, 1 of 1)

- [Learning Flexible Generalization in Video Quality Assessment by Bringing Device and Viewing Condition Distributions](https://mlsanity.com/paper/2607.04643/) (2026)

## Abstract (arXiv)

Over the past decades, numerous Image Quality Assessment (IQA) models have emerged, aiming to predict the perceptual quality of images. However, individual models are often biased toward certain types of image content or distortions, depending on the design principle and process. An intuitive idea is to harness the strengths and mitigate the weaknesses of each IQA model, by fusing the scores of multiple models into a stronger one. Here we make one of the first attempts to seek an optimal solution for the idea and propose a general framework for unsupervised IQA score fusion using deep Maximum a Posteriori (MAP) estimation. The proposed model conducts fine-grained uncertainty estimation at the score level to increase the accuracy and reduce the uncertainty in fused predictions. Comprehensive experiments demonstrate the superiority of the proposed model over individual IQA models and other fusion methods. It also exhibits an interesting capability of rejecting ``bad" models in the fusion process.