Visibility and Distortion Measurement for No-Reference Dehazed Image Quality Assessment via Complex Contourlet Transform
Tuxin Guan, Chaofeng Li, Ke Gu, Hantao Liu, Yuhui Zheng, Xiao‐Jun Wu
Abstract
Recently, most dehazed image quality assessment (DQA) methods have focused on estimating remaining haze and omitting distortion impact from the side effect of dehazing algorithms, which leads to their limited performance. Addressing this problem, we propose a method for learning both visibility and distortion-aware features no-reference (NR) dehazed image quality assessment (VDA-DQA). Visibility-aware features are exploited to characterize clarity optimization after dehazing, including the brightness-, contrast-, and sharpness-aware features extracted by the complex contourlet transform (CCT). Then, distortion-aware features are employed to measure the distortion artifacts of images, including the normalized histogram of the local binary pattern (LBP) from the reconstructed dehazed image and the statistics of the CCT subbands corresponding to the chroma and saturation map. Finally, all the above features are mapped into quality scores by support vector regression (SVR). Extensive experimental results on six public DQA datasets verify the superiority of the proposed VDA-DQA method in terms of consistency with subjective visual perception and outperform state-of-the-art methods.