Haze level evaluation is highly desired in outdoor scene monitoring applications. However, there are relatively few approaches available in this area. In this paper, a novel haze level evaluation strategy for real-world outdoor scenes is presented. The idea is inspired by the utilization of dark and bright channel prior (DBCP) for haze removal. The change between hazy and haze-free scenes in bright channels could serve as a haze level indicator, and we have named it DBCP-I. The variation of contrast between dark and bright channels in a single hazy image also contains useful information to reflect haze level. By searching for a segmentation threshold, a metric called DBCP-II is proposed. Combining the strengths of the above two indicators, a hybrid metric named DBCP-III is constructed to achieve better performance. The experiment results on public, real-world benchmark datasets show the advantages of the proposed methods in terms of assessment accuracy with subjective human ratings. The study is first-of-its-kind with preliminary exploration in the field of haze level evaluation for real outdoor scenes, and it has a great potential to promote research in autonomous driving and automatic air quality monitoring. The open-source codes of the proposed algorithms are free to download.