Visual Saliency Detection Algorithm in Compressed HEVC Domain

Abstract

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.