Debiased Subjective Assessment of Real-World Image Enhancement

Subjective assessment of AI image enhancement tools is prone to multiple biases: The paper identifies three specific sources of bias in how image enhancement algorithms are currently evaluated—sampling bias (too few examples), algorithmic bias (overfitting to selected samples), and subjective bias (cherry-picking favorable results)—which can make evaluation more "art than science."

Current evaluation practices may not reflect real-world performance: Conventional testing manually selects a small set of visual examples that may not represent the full diversity of images users encounter, potentially leading to inflated or misleading assessments of how well enhancement tools actually work.

Automated, diverse sample selection improves evaluation fairness: The paper proposes automatically selecting test images based on two criteria—maximizing differences between competing algorithms and ensuring diversity among test images—which helps reduce human bias in choosing which examples to evaluate.

The method applies across multiple real-world applications: The debiasing approach was tested on three practical image enhancement tasks (dehazing, super-resolution, and low-light enhancement), suggesting the framework could improve evaluation standards across different AI image processing tools.

Better evaluation methods are essential for responsible AI deployment: By addressing biases in how image enhancement algorithms are assessed, this work supports more transparent and trustworthy evaluation practices, helping ensure that AI tools recommended to users have been fairly and comprehensively tested.