A Model of Saliency-Based Visual Attention for Rapid Scene Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Visual attention detection in video sequences using spatiotemporal cues
MULTIMEDIA '06 Proceedings of the 14th annual ACM international conference on Multimedia
Texture features for DCT-coded image retrieval and classification
ICASSP '99 Proceedings of the Acoustics, Speech, and Signal Processing, 1999. on 1999 IEEE International Conference - Volume 06
IEEE Transactions on Image Processing
Saliency-based image retargeting in the compressed domain
MM '11 Proceedings of the 19th ACM international conference on Multimedia
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Saliency detection is widely used to extract the regions of interest in images. Many saliency detection models have been proposed for videos in the uncompressed domain. However, videos are always stored in the compressed domain such as MPEG2, H.264, MPEG4 Visual, etc. In this study, we propose a video saliency detection model based on feature contrast in the compressed domain. Four features of luminance, color, texture and motion are extracted from DCT coefficients and motion vectors in the video bitstream. The static saliency map of video frames is calculated based on the luminance, color and texture features, while the motion saliency map for video frames is computed by motion feature. The final saliency map for video frames is obtained through combining the static saliency map and motion saliency map. Experimental results show good performance of the proposed video saliency detection model in the compressed domain.