3871

Fast JND-Based Video Carving With GPU Acceleration for Real-Time Video Retargeting

Chen-Kuo Chiang, Shu-Fan Wang, Yi-Ling Chen, Shang-Hong Lai
Department of Computer Science, National Tsing Hua University, Hsinchu, Taiwan
IEEE Transactions on Circuits and Systems for Video Technology, 2009

@article{chiang2009fast,

   title={Fast JND-based video carving with GPU acceleration for real-time video retargeting},

   author={Chiang, C.K. and Wang, S.F. and Chen, Y.L. and Lai, S.H.},

   journal={Circuits and Systems for Video Technology, IEEE Transactions on},

   volume={19},

   number={11},

   pages={1588–1597},

   issn={1051-8215},

   year={2009},

   publisher={IEEE}

}

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A recently developed image resizing technique, seam carving, has been proved to be a useful tool for content-adaptive spatially nonuniform image resizing with the purpose of optimal display on a screen of reduced resolution or different aspect ratio. In this paper, we present a fast algorithm for real-time content-aware video retargeting based on the improved seam carving method proposed in this paper. The proposed algorithm is designed to be highly parallelizable and suitable for running on a multicore architecture. First, two novel operators, i.e., seam update and seam split, are introduced to analyze an image for detecting the local and global seams with minimum costs very efficiently. With these operators, parallel processing can be achieved to determine multiple seams simultaneously. In addition, the saliency measure is extended with a just-noticeable-distortion model which makes the resized video more consistent with human perception. We demonstrate the efficiency of the above new components with a graphics processing unit (GPU) implementation. In addition, the proposed fast seam carving algorithm is extended for video retargeting. To the best of our knowledge, this is the first paper based on the seam carving method to achieve real-time video retargeting on a GPU. Experimental results on video sequences of various characteristics are demonstrated to show the superior performance of the proposed algorithm in comparison with the existing content-adaptive image/video resizing methods.
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