中文摘要：

视频中时间域上的闪烁干扰是影响视频质量的因素之一,对闪烁干扰的准确辨识不但有利于视频质量的自动分析与诊断,而且能够与干扰去除和质量增强算法形成反馈机制,促进此类算法的自适应性。以监控视频中的闪烁干扰为研究对象,研究发现闪烁干扰信号的差分信号服从拉普拉斯分布。基于拉普拉斯分布的特性和小概率事件的思想,在视频时间域差分信号中采用迭代拟合方法,有效地分离出差分信号中影响闪烁干扰辨识的客观运动差分信号;同时,运用人眼视觉系统的最小可感知差异（JND）理论,通过量化闪烁干扰的频率和闪烁幅度得到闪烁干扰辨识指标,对闪烁干扰进行了有效的无参考辨识,并通过受试者工作特征（ROC）曲线验证了提出的无参考闪烁干扰辨识指标在正负样本的分类上具有比较理想的效果,提出的无参考算法在闪烁干扰辨识方面具有较好的性能。

英文摘要：

Temporal flickering in the video is a key factor of affecting the quality of video. Accurate identification of temporal flickering is required for the automatic analysis and diagnosis of video quality. Moreover, it can be integrated with artifact removal and quality enhancement algorithms to promote the adaptivity of the proposed algorithm. A study of temporal flickering in video surveillance was given to demonstrate that the differential signal of temporal flickering in time domain follows the Laplacian distribution. Motivated by this statistical observation and the idea of small probability events, the proposed method iteratively segmented differential signal of motion in foreground, which affected the identification of temporal flickering. Furthermore, the proposed approach exploited the Just-Noticeable Difference （JND） mechanism of the human visual system to identify the temporal flickering using the flickering frequency and amplitude. The proposed method yielded superior performance to that of the conventional Gaussian Mixture model to achieve more accurate classification of the normal video and temporal flickering video, as verified in the ROC （ Receiver Operating Characteristic） curve presented in experimental results. The proposed no-reference algorithm is able to achieve fairly good performance in temporal flickering identification.