Visibility and distortion measurement for no-reference dehazed image quality assessment via complex contourlet transform

Recently, most dehazed image quality assessment (DQA) methods mainly focus on the estimation of remaining haze, omitting the impact of distortions from the side effect of dehazing algorithms, which lead to their limited performance. Addressing this problem, we proposed a learning both Visibility and Distortion Aware features no-reference (NR) Dehazed image Quality Assessment method (VDA-DQA). Visibility aware features are exploited to characterize clarity optimization after dehazing, including the brightness, contrast, and sharpness aware feature extracted by complex contourlet transform (CCT). Then, distortion aware features are employed to measure the distortion artifacts of images, including the normalized histogram of local binary pattern (LBP) from the reconstructed dehazed image and the statistics of the CCT sub-bands corresponding to chroma and saturation map. Finally, all the above features are mapped into the quality scores by the support vector regression (SVR). Extensive experimental results on six public DQA datasets verify the superiority of proposed VDA-DQA in terms of the consistency with subjective visual perception, and outperforms the state-of-the-art methods.The source code of VDA-DQA is available at https://github.com/li181119/VDA-DQA