Learning-based haze visibility ranking score for real-world traffic surveillance images

Image-based quantified haze visibility evaluation is crucial for both atmospheric science and computer vision. Existing methods typically rely on datasets with precisely annotated visibility labels. However, the scarcity of real-world labeled data significantly hinders their scalability and practical deployment. To address this, we introduce HZSC, a real-world haze visibility dataset collected from traffic surveillance cameras. It consists of 36 diverse outdoor scenes, each containing 20 images ranked by haze visibility levels. Meanwhile, we propose the haze visibility evaluator (HVE) for referenceless image haze visibility assessment. HVE comprises three key components: a depth feature extractor, a haze visibility feature extractor, and a feature fusion module. The fused features are fed into learned fog-free and foggy Multivariate Gaussian (MVG) models to generate haze visibility scores. For training, We design a scoring-ranking framework using pairwise supervision, adopting a weighted hinge ranking loss to improve discrimination of subtle visibility variations. Extensive experiments on both the public MRFID dataset and our collected HZSC dataset demonstrate that our HVE consistently outperforms existing state-of-the-art methods. Examples of our dataset can be available at https://github.com/ZanChen1/HLVRS.