基于超像素显著性的视频偏色检测方法

摘要

针对传统偏色检测方法存在的局限性,基于超像素显著性,提出一种视频偏色检测方法.将视频图像分割为超像素块,提取超像素块的亮度显著性和颜色显著性.使用亮度显著性权值系数对颜色显著性进行贝叶斯融合处理,得到综合显著的超像素块特征信息.将块特征向量作为训练样本,使用支持向量机进行分类和回归训练,检测出偏色视频并实现视频的偏色程度评分.实验结果表明,与传统偏色检测方法相比,该方法检测偏色视频的准确率更高,达到95.1％,且偏色程度的检测结果更接近人眼主观评价结果.%For the limitations of the traditional color cast detection methods,this paper proposes a new color cast detection method based on super-pixel saliency.The video image is divided into super-pixel blocks whose luminance and color saliency are extracted.Then,the luminance saliency weight coefficient is used to realize Bayesian fusion of color saliency to get comprehensive and significant feature information.The block feature vectors are used as training samples,and Support Vector Machine(SVM) is used to reallize classification and regression training to detect color cast video and score of the degree of the video color cast.Experimental result shows that,compared with traditional color cast detection methods,the proposed method gets 95.1％ accuracy and the dection result is more consistent to human eye subjective evaluations.