No-reference quality evaluation of realistic hazy images via singular value decomposition

Haze is one of the atmospheric image degradations that causes severe distortions to outdoor images such as low contrast, color shift, and structure damage. Due to the unique physical characteristics of haze, the quality of hazy images is not accurately assessed using general-purpose image quality assessment (IQA) approaches. Therefore, several haze-aware IQA approaches have been proposed to provide more efficient dehazing quality evaluation. These approaches extract several haze-aware features to be either combined to form a single IQA metric or fed to a regression model that predicts the dehazing quality. However, these haze-relevant features are extracted using pixel intensity, in which luminance and structure information are inseparable, leading to less correlation between such features and the type of degradation they are supposed to represent. To address this issue, we propose a singular value decomposition (SVD) based IQA metric that can effectively separate the luminance component of an image from structure. This separation offers the ability to accurately evaluate the degradation at two different levels i.e. luminance and structure. The experimental results show that our proposed SVD-based dehazing quality evaluator (SDQE) outperforms the existing state-of-the-art non-reference IQA metrics in terms of accuracy and processing time.