@inproceedings{
zhou2024towards,
title={Towards Distortion-Debiased  Blind Image Quality  Assessment},
author={Lize Zhou and Xiaoqi Wang and Jian Xiong and Xianzhong Long and Hao Gao},
booktitle={ACM Multimedia 2024},
year={2024},
url={https://openreview.net/forum?id=6ko0tOQllI}
}
Existing blind image quality assessment (BIQA) models are susceptible to biases related to distortion intensity and domain. Intensity bias manifests as an over-sensitivity to severe distortions and under-estimation of minor ones, while domain bias stems from the discrepancies between synthetic and authentic distortion properties. This work introduces a unified learning framework to address these distortion biases. We integrate distortion perception and restoration modules to address intensity bias. The restoration module uses a combined image-level and feature-level denoising method to restore distorted images, where easily restorable minor distortions serve as references for mildly distorted images, and severe distortions benefit directly from distortion perception. Finally, calculating a distortion intensity matrix via intensity-aware cross-attention for adaptive handling of intensity bias. To tackle domain bias, we introduce a distortion domain recognition task, leveraging inherent differences between synthetic and authentic distortions for adaptive quality score weighting. Experimental results show that our proposed method achieves state-of-the-art performance on a multitude of synthetic and authentic IQA benchmark datasets. The code and models will be available.
Towards Distortion-Debiased  Blind Image Quality  Assessment
Lize Zhou Xiaoqi Wang Jian Xiong Xianzhong Long Hao Gao