MSTRIQ: No Reference Image Quality Assessment Based on Swin Transformer with Multi-Stage Fusion

TL;DR

This paper introduces MSTRIQ, a no-reference image quality assessment method using Swin Transformer with multi-stage feature fusion, effective data augmentation, and ranking-based optimization, achieving state-of-the-art results on standard datasets.

Contribution

The paper presents a novel Swin Transformer-based IQA model with multi-stage feature fusion and ranking optimization, addressing dataset limitations and improving performance.

Findings

Outperforms previous methods on standard IQA datasets.

Ranks 2nd in NTIRE 2022 IQA challenge no-reference track.

Demonstrates robustness across diverse image distortions.

Abstract

Measuring the perceptual quality of images automatically is an essential task in the area of computer vision, as degradations on image quality can exist in many processes from image acquisition, transmission to enhancing. Many Image Quality Assessment(IQA) algorithms have been designed to tackle this problem. However, it still remains un settled due to the various types of image distortions and the lack of large-scale human-rated datasets. In this paper, we propose a novel algorithm based on the Swin Transformer [31] with fused features from multiple stages, which aggregates information from both local and global features to better predict the quality. To address the issues of small-scale datasets, relative rankings of images have been taken into account together with regression loss to simultaneously optimize the model. Furthermore, effective data augmentation strategies are also used to improve the performance. In comparisons with previous works, experiments are carried out on two standard IQA datasets and a challenge dataset. The results demonstrate the effectiveness of our work. The proposed method outperforms other methods on standard datasets and ranks 2nd in the no-reference track of NTIRE 2022 Perceptual Image Quality Assessment Challenge [53]. It verifies that our method is promising in solving diverse IQA problems and thus can be used to real-word applications.