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http://dx.doi.org/10.6109/jkiice.2020.24.2.231

Hybrid Blending for Video Composition  

Kim, Jihong (Department of Electronic Engineering, Dong-Eui University)
Heo, Gyeongyong (Department of Electronic Engineering, Dong-Eui University)
Publication Information
Journal of the Korea Institute of Information and Communication Engineering / v.24, no.2, 2020 , pp. 231-237 More about this Journal
Abstract
In this paper, we provide an efficient hybrid video blending scheme to improve the naturalness of composite video in Poisson equation-based composite methods. In image blending process, various blending methods are used depending on the purpose of image composition. The hybrid blending method proposed in this paper has the characteristics that there is no seam in the composite video and the color distortion of the object is reduced by properly utilizing the advantages of Poisson blending and alpha blending. First, after blending the source object by the Poisson blending method, the color difference between the blended object and the original object is compared. If the color difference is equal to or greater than the threshold value, the object of source video is alpha blended and is added together with the Poisson blended object. Simulation results show that the proposed method has not only better naturalness than Poisson blending and alpha blending, but also requires a relatively small amount of computation.
Keywords
alpha blending; Poisson blending; hybrid blending; video composition;
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Times Cited By KSCI : 2  (Citation Analysis)
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1 M. Afifi, K. F. Hussain, H. M. Ibrahim, and N. M. Omar, "Video Face Replacement System Using a Modified Poisson Blending Technique," International Symposium on Intelligent Signal Processing and Communication Systems, pp.1-5, 2015.
2 I. S. Sevcenco, and P. Agathoklis, "Video Editing in Gradient Domain Using a Wavelet based 3-D Reconstruction Algorithm and an Iterative Poisson Solver," IEEE Pacific Rim Conference on Communications, Computers and Signal Processing, pp. 205-209, 2015.
3 W.S. Mokrzycki, and M. Tatol, "Colour difference ΔE - A survey," Machine Graphics and Vision, vol.20, pp.383-411, Apr. 2011.
4 M. Afifi, and K. F. Hussain, "MPB: A modified Poisson blewnding technique," Computational Visual Media, vol.1, no.4, pp.331-341, Dec. 2015.   DOI
5 X. Bai, J. Wang, D. Simons, and G. Sapiro, "Video Snapcut: Robust Video Object Cutout Using Localized Classifiers," ACM Transactions on Graphics, vol. 28, no. 3, pp. 1-11, 2009.
6 P. Perez, M. Gangnet, and A. Blake, "Poisson Image Editing," ACM Transaction on Graphics, vol. 22, no. 3, pp.313-318, Jul. 2003.   DOI
7 T. Chen, J. Y. Zhu, A. Shamir, and S.M. Hu, "MotionAware Gradient Domain Video Composition," IEEE Transactions on Image Processing, vol. 22, no. 7, pp. 2532-2544, Jul. 2013.   DOI
8 G. Heo, H. Choi, and J. Kim, "Poisson Video Composition Using Shape Matching," Journal of the Korea Institute of Information and Communication Engineering, vol.22, no.4, pp.617-623, 2018.   DOI
9 Richard J. Radke, Computer Vision for Visual Effects, Cambridge university press, 2013.
10 K. F. Hussain, and R. M. Kamel, "Efficient Poisson Image Editing," Electronic Letters on Computer Vision and Image Analysis, 14(2), pp. 45-57, 2015.