A Video Saliency Detection Model in Compressed Domain

Saliency detection is widely used to extract regions of interest in images for various image processing applications. Recently, many saliency detection models have been proposed for video in uncompressed (pixel) domain. However, video over Internet is always stored in compressed domains, such as MPE...

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Bibliographic Details
Published in:IEEE transactions on circuits and systems for video technology 2014-01, Vol.24 (1), p.27-38
Main Authors: YUMING FANG, WEISI LIN, ZHENZHONG CHEN, TSAI, Chia-Ming, LIN, Chia-Wen
Format: Article
Language:English
Subjects:
Applied sciences
Compressed domain
Computational modeling
Detection, estimation, filtering, equalization, prediction
Discrete cosine transforms
Exact sciences and technology
Feature extraction
Image coding
Image color analysis
Image processing
Information, signal and communications theory
Interconnected networks
Networks and services in france and abroad
Signal and communications theory
Signal processing
Signal, noise
Telecommunications
Telecommunications and information theory
Teleprocessing networks. Isdn
Vectors
video saliency detection
visual attention
Visualization
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Summary:Saliency detection is widely used to extract regions of interest in images for various image processing applications. Recently, many saliency detection models have been proposed for video in uncompressed (pixel) domain. However, video over Internet is always stored in compressed domains, such as MPEG2, H.264, and MPEG4 Visual. In this paper, we propose a novel video saliency detection model based on feature contrast in compressed domain. Four types of features including luminance, color, texture, and motion are extracted from the discrete cosine transform coefficients and motion vectors in video bitstream. The static saliency map of unpredicted frames (I frames) is calculated on the basis of luminance, color, and texture features, while the motion saliency map of predicted frames (P and B frames) is computed by motion feature. A new fusion method is designed to combine the static saliency and motion saliency maps to get the final saliency map for each video frame. Due to the directly derived features in compressed domain, the proposed model can predict the salient regions efficiently for video frames. Experimental results on a public database show superior performance of the proposed video saliency detection model in compressed domain.
ISSN:1051-8215
1558-2205
DOI:10.1109/TCSVT.2013.2273613