• 中国计算机学会会刊
  • 中国科技核心期刊
  • 中文核心期刊

计算机工程与科学 ›› 2021, Vol. 43 ›› Issue (07): 1283-1290.

• 图形与图像 • 上一篇    下一篇

基于全局和局部特征的无参考夜间图像质量评价

赵月,王来花,齐苏敏,王伟胜,刘晨   

  1. (曲阜师范大学网络空间安全学院,山东 曲阜 273165)
  • 收稿日期:2020-05-29 修回日期:2020-07-30 接受日期:2021-07-25 出版日期:2021-07-25 发布日期:2021-08-17
  • 基金资助:
    国家自然科学基金(61601261)

No-reference quality assessment of night-time images based on global and local features

ZHAO Yue,WANG Lai-hua,QI Su-min,WANG Wei-sheng,LIU Chen   

  1. (School of Cyberspace Security,Qufu Normal University,Qufu 273165,China)
  • Received:2020-05-29 Revised:2020-07-30 Accepted:2021-07-25 Online:2021-07-25 Published:2021-08-17

摘要: 针对夜间图像光线暗、特征不易提取的问题,提出一种基于全局和局部特征的无参考夜间图像质量评价方法。首先,利用等高线原理将图像分为亮区域和暗区域2部分,将亮区域占整幅图的比例作为特征1;其次,提取夜间图像的全局亮度信息并将其作为特征2;再次,结合微分算子法求得图像的边缘图作为特征3;然后,将夜间图像从RGB转换到HSI,提取色度、饱和度和亮度分量并将其分别作为特征4、特征5和特征6;最后,结合上述特征通过BP神经网络建立评价模型来评价夜间图像的质量。在公开数据库上的测试结果表明,所提方法与主观分数具有更好的一致性,并且优于现有的图像质量评价方法。


关键词: 夜间图像, 质量评价, 局部亮区域, 边缘检测

Abstract: To solve the problems of dark light and difficult feature extraction of night-time images, a no-reference night-time image quality evaluation method based on global and local features is proposed. Firstly, the contour principle is used to divide the image into light region and dark region, and the proportion of the bright region is taken as feature1. Secondly, the global brightness information of the night-time image is extracted and used as feature2. Then, the differential operator method is adopted to obtain the edge of the image as feature3. Finally, the night-time image is converted from RGB to HSI, and the hue, saturation and brightness components are extracted as feature4, feature5 and feature6. Combining the above features, an evaluation model is established by BP neural network to evaluate the quality of night-time images. The test results on the public database show that the proposed method is more consistent with the subjective score and better than the existing image quality evaluation methods.


Key words: night-time image, quality evaluation, local bright region, edge detection