# No-Reference Image Quality Assessment via Feature Fusion and Multi-Task Learning

arXiv: https://arxiv.org/abs/2006.03783 · HTML: https://mlsanity.com/paper/2006.03783/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Jun 2020.

## Built on by (most cited first, 4 of 4)

- [GPA-Net:No-Reference Point Cloud Quality Assessment with Multi-task Graph Convolutional Network](https://mlsanity.com/paper/2210.16478/) (2022)
- [Subjective and Objective Quality Assessment for in-the-Wild Computer Graphics Images](https://mlsanity.com/paper/2303.08050/) (2023)
- [Helping Visually Impaired People Take Better Quality Pictures](https://mlsanity.com/paper/2305.08066/) (2023)
- [Weighted Fuzzy-Based PSNR for Watermarking](https://mlsanity.com/paper/2101.08502/) (2021)

## Abstract (arXiv)

Blind or no-reference image quality assessment (NR-IQA) is a fundamental, unsolved, and yet challenging problem due to the unavailability of a reference image. It is vital to the streaming and social media industries that impact billions of viewers daily. Although previous NR-IQA methods leveraged different feature extraction approaches, the performance bottleneck still exists. In this paper, we propose a simple and yet effective general-purpose no-reference (NR) image quality assessment (IQA) framework based on multi-task learning. Our model employs distortion types as well as subjective human scores to predict image quality. We propose a feature fusion method to utilize distortion information to improve the quality score estimation task. In our experiments, we demonstrate that by utilizing multi-task learning and our proposed feature fusion method, our model yields better performance for the NR-IQA task. To demonstrate the effectiveness of our approach, we test our approach on seven standard datasets and show that we achieve state-of-the-art results on various datasets.