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Visual Saliency Detection Algorithm in Compressed HEVC Domain

机译：压缩HEVC域中的视觉显着性检测算法

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摘要

Saliency detection has been widely used to predict human fixation. In this paper, a Visual Saliency Detection Algorithm in Compressed HEVC Domain is proposed which consists of three parts: static saliency detection, dynamic saliency detection and competitive fusion. Firstly, the Gauss model is used to filter out the background of the static features which are extracted by down-sampling and DCT. Secondly, the motion vectors are used to represent the dynamic feature. Then the dynamic saliency is calculated by filtering out the background of dynamic feature. Finally, the competitive fusion model is used to adaptively combine the characteristic of static and dynamic saliency maps. Experimental results show that the proposed method is superior to classic state-of-the-art saliency detection methods with 0.05 AUC value increasing and 0.17 KL divergence decreasing on average. The average time of one frame detection is 2.3 seconds.
机译：显着性检测已被广泛用于预测人类固定。本文提出了一种压缩的HEVC域中的视觉显着性检测算法，其包括三个部分：静态显着性检测，动态显着性检测和竞争融合。首先，Gauss模型用于过滤通过下采样和DCT提取的静态特征的背景。其次，运动向量用于表示动态特征。然后通过过滤动态特征的背景来计算动态显着性。最后，竞争融合模型用于自适应地结合静态和动态显着图的特性。实验结果表明，该方法优于经典的最先进的耐药性检测方法，随着0.05 AUC值的增加，0.17kL发散减少。一个帧检测的平均时间为2.3秒。

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