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. An 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.

中文翻译：

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基于场景理解的双波段彩色夜视融合图像质量客观评估研究

双频色彩融合图像的色彩质量和清晰度对观众对场景内容的理解影响最大。传统的图像质量评估（IQA）方法侧重于图像的亮度对比度，而对像素关系的关注有限。因此，这些方法在与颜色相关的失真方面表现不佳。提出了一种融合图像综合质量客观评估模型。该模型根据灰度共生矩阵（GLCM）与图像颜色之间的相关性，提取GLCM的均匀性、角二阶矩和对比度作为图像颜色质量特征。梯度加权类激活映射用于计算热图，反映每个区域的视觉显着性。我们使用每个区域的视觉显着性权重来对相应区域的旋转和均匀不变局部二值模式直方图进行加权，以获得清晰度特征。实验结果表明，该方法的性能优于现有的8种无参考IQA模型。