Research on objective assessment of dual-band color night vision fusion image quality based on scene understanding

The color quality and sharpness of the dual-band color fusion images have the greatest impact on the audience's understanding of the scene content. Traditional image quality assessment (IQA) methods focus on the luminance contrast of image and attach limited attention to pixel-wise relationship. As a result, these methods perform poorly on color-related distortions. A objective assessment model for the comprehensive quality of fusion images is proposed. According to the correlation between the gray level co-occurrence matrix (GLCM) and the image color, the model extracts the homogeneity, angular second moment, and contrast of the GLCM as the image color quality features. Gradient-weighted class activation mapping is used to calculate the heat map, reflecting the visual saliency of each region. We use the visual saliency weight of each region to weight the rotation and uniform invariant local binary pattern histograms of the corresponding regions to obtain sharpness features. The experimental results show that the performance of the proposed method is better than the existing eight no-reference IQA models.