Preference-Guided Debiasing for No-Reference Enhancement Image Quality Assessment

Abstract

Current no-reference image quality assessment (NR-IQA) models for enhanced images often struggle to generalize, as they tend to overfit to the distinct patterns of specific enhancement algorithms rather than evaluating genuine perceptual quality. To address this issue, we propose a preference-guided debiasing framework for no-reference enhancement image quality assessment (EIQA). Specifically, we first learn a continuous enhancement-preference embedding space using supervised contrastive learning, where images generated by similar enhancement styles are encouraged to have closer representations. Based on this, we further estimate the enhancement-induced nuisance component contained in the raw quality representation and remove it before quality regression. In this way, the model is guided to focus on algorithm-invariant perceptual quality cues instead of enhancement-specific visual fingerprints. To facilitate stable optimization, we adopt a two-stage training strategy that first learns the enhancement-preference space and then performs debiased quality prediction. Extensive experiments on public EIQA benchmarks demonstrate that the proposed method effectively mitigates algorithm-induced representation bias and achieves superior robustness and cross-algorithm generalization compared with existing approaches.

Figures (4)

• Figure 1: Different enhancement algorithms may produce distinct visual styles, causing algorithm-dependent bias in quality representation. The proposed preference-guided debiasing framework explicitly models enhancement preference and removes its interference, leading to more reliable perceptual quality prediction.
• Figure 2: Overall framework of the proposed method. First, a preference encoder is pretrained with supervised contrastive learning to capture enhancement-preference representations. Second, the learned preference cue is fed into a bias predictor to estimate the bias vector, which is then subtracted from the raw quality feature to obtain a debiased representation for MOS regression.
• Figure 3: Illustration of the proposed preference-guided debiasing method for enhancement image quality assessment. The model first learns a preference embedding from enhanced images to characterize algorithm-dependent enhancement styles, then predicts the corresponding bias term and removes it from the raw quality feature, yielding an algorithm-invariant quality representation for more reliable MOS prediction.
• Figure 4: Qualitative comparison on images enhanced by different algorithms. Images in each row share the same source content but exhibit different enhancement styles. Although their MOS values are relatively similar, the model without preference guidance produces inconsistent predictions, while the proposed method yields predictions that are more stable and closer to the ground-truth MOS. Red numbers indicate relatively large prediction errors.