ZTE Communications ›› 2019, Vol. 17 ›› Issue (1): 31-37.DOI: 10.12142/ZTECOM.201901006

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Visual Attention Modeling inCompressed Domain:From Image Saliency Detection toVideo Saliency Detection

FANG Yuming, ZHANG Xiaoqiang   

  1. Jiangxi University of Finance and Economics, Nanchang, Jiangxi 330032, China
  • Received:2018-07-19 Online:2019-02-20 Published:2019-11-14
  • About author:FANG Yuming (fa0001ng@e.ntu.edu.sg) received his Ph.D. degree from Nanyang Technological University, Singapore, M.S. degree from Beijing University of Technology, China, and B.E. degree from Sichuan University, China. Currently, he is a professor in the School of Information Management, Jiangxi University of Finance and Economics, China. He serves as an associate editor of IEEE Access and is on the editorial board of Signal Processing: Image Communication. His research interests include visual attention modeling, visual quality assessment, image retargeting, computer vision, 3D image/video processing, etc.|ZHANG Xiaoqiang is currently pursuing the master’s degree with the School of Information Technology, Jiangxi University of Finance and Economics, China. His research interests include saliency detection, computer vision, machine learning, and deep learning.

Abstract:

Saliency detection models, which are used to extract salient regions in visual scenes, are widely used in various multimedia processing applications. It has attracted much attention in the area of computer vision over the past decades. Since most images or videos over the Internet are stored in compressed domains such as images in JPEG format and videos in MPEG2 format, H.264 format, and MPEG4 Visual format, many saliency detection models have been proposed in the compressed domain recently. We provide a review of our works on saliency detection models in the compressed domain in this paper. Besides, we introduce some commonly used fusion strategies to combine spatial saliency map and temporal saliency map to compute the ?nal video saliency map.

Key words: saliency detection, computer vision, compressed domain, visual attention, fusion strategy