# FUNQUE: Fusion of Unified Quality Evaluators

arXiv: https://arxiv.org/abs/2202.11241 · HTML: https://mlsanity.com/paper/2202.11241/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 23 references that are in the map.
- First posted on arXiv 23 Feb 2022.

## Builds on (most cited first, 5 of 5)

- [SpatioTemporal Feature Integration and Model Fusion for Full Reference Video Quality Assessment](https://mlsanity.com/paper/1804.04813/) (2018)
- [A Hitchhiker's Guide to Structural Similarity](https://mlsanity.com/paper/2101.06354/) (2021)
- [Perceptual Video Quality Prediction Emphasizing Chroma Distortions](https://mlsanity.com/paper/2009.11203/) (2020)
- [Enhancing VMAF through New Feature Integration and Model Combination](https://mlsanity.com/paper/2103.06338/) (2021)
- [Deep Video Precoding](https://mlsanity.com/paper/1908.00812/) (2019)

## Built on by (most cited first, 8 of 8)

- [ColorVideoVDP: A visual difference predictor for image, video and display distortions](https://mlsanity.com/paper/2401.11485/) (2024)
- [Video Quality Assessment: A Comprehensive Survey](https://mlsanity.com/paper/2412.04508/) (2024)
- [A FUNQUE Approach to the Quality Assessment of Compressed HDR Videos](https://mlsanity.com/paper/2312.08524/) (2023)
- [Subjective Quality Assessment of Compressed Tone-Mapped High Dynamic Range Videos](https://mlsanity.com/paper/2403.15061/) (2024)
- [Evaluating quality metrics through the lenses of psychophysical measurements of low-level vision](https://mlsanity.com/paper/2503.16264/) (2025)
- [Joint Quality Assessment and Example-Guided Image Processing by Disentangling Picture Appearance from Content](https://mlsanity.com/paper/2404.13484/) (2024)
- [Cut-FUNQUE: An Objective Quality Model for Compressed Tone-Mapped High Dynamic Range Videos](https://mlsanity.com/paper/2404.13452/) (2024)
- [A Parametric Bi-Directional Curvature-Based Framework for Image Artifact Classification and Quantification](https://mlsanity.com/paper/2508.08824/) (2025)

## Abstract (arXiv)

Fusion-based quality assessment has emerged as a powerful method for developing high-performance quality models from quality models that individually achieve lower performances. A prominent example of such an algorithm is VMAF, which has been widely adopted as an industry standard for video quality prediction along with SSIM. In addition to advancing the state-of-the-art, it is imperative to alleviate the computational burden presented by the use of a heterogeneous set of quality models. In this paper, we unify "atom" quality models by computing them on a common transform domain that accounts for the Human Visual System, and we propose FUNQUE, a quality model that fuses unified quality evaluators. We demonstrate that in comparison to the state-of-the-art, FUNQUE offers significant improvements in both correlation against subjective scores and efficiency, due to computation sharing.