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Adaptive Video Super-Resolution Based on Superpixel-Guided Auto-Regressive Model

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Advances in Multimedia Information Processing – PCM 2013 (PCM 2013)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 8294))

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Abstract

This paper proposes an adaptive video super-resolution (SR) method based on superpixel-guided auto-regressive (AR) model. The key-frames are automatically selected and super-resolved by a sparse regression method. The non-key-frames are super-resolved by simultaneously exploiting the spatiotemporal correlations: the temporal correlation is exploited by a GPU-based optical flow method while the spatial correlation is modelled by a superpixel-guided AR model. Experimental results show that the proposed method outperforms the existing benchmark in terms of both subjective visual quality and objective peak-to-peak signal-to-noise ratio (PSNR). At the same time, the running time of the proposed method is the shortest in comparison with the state-of-the-art methods, which makes the proposed method suitable for practical applications.

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References

  1. Achanta, R., Shaji, A., Smith, K., Lucchi, A., Fua, P., Süsstrunk, S.: SLIC superpixels. École Polytechnique Fédéral de Lausssanne (EPFL), Tech. Rep. 149300 (2010)

    Google Scholar 

  2. Altunbasak, Y., Patti, A.J., Mersereau, R.M.: Super-resolution still and video reconstruction from MPEG-coded video. IEEE Trans. Circuits and Systems for Video Technology 12(4), 217–226 (2002)

    Article  Google Scholar 

  3. Belekos, S., Galatsanos, N.P., Katsaggelos, A.K.: Maximum a posteriori video super-resolution using a new multichannel image prior. IEEE Trans. Image Processing 19(6), 1451–1464 (2010)

    Article  MathSciNet  Google Scholar 

  4. Brandi, F., Queiroz, R., Mukherjee, D.: Super-resolution of video using key frames and motion estimation. In: Proc. IEEE International Conference on Image Processing, pp. 321–324 (October 2008)

    Google Scholar 

  5. Brandi, F., de Queiroz, R., Mukherjee, D.: Super resolution of video using key frames. In: Proc. IEEE International Symposium on Circuits and Systems, pp. 1608–1611. IEEE (2008)

    Google Scholar 

  6. Brandi, F., de Queiroz, R., Mukherjee, D.: Super-resolution of video using key frames and motion estimation. In: Proc. IEEE International Conference on Image Processing, pp. 321–324 (October 2008)

    Google Scholar 

  7. Choi, B.D., Han, J.W., Kim, C.S., Ko, S.J.: Motion-compensated frame interpolation using bilateral motion estimation and adaptive overlapped block motion compensation. IEEE Trans. Circuits and Systerms for Video Technology 17(4), 407–416 (2007)

    Article  Google Scholar 

  8. Eisemann, M., De Decker, B., Magnor, M., Bekaert, P., De Aguiar, E., Ahmed, N., Theobalt, C., Sellent, A.: Floating textures. In: Proc. Computer Graphics Forum, vol. 27, pp. 409–418. Wiley Online Library (2008)

    Google Scholar 

  9. Fan, W., Yeung, D.Y.: Image hallucination using neighbor embedding over visual primitive manifolds. In: Proc. IEEE Conf. Computer Vision and Pattern Recognition, pp. 1–7 (2007)

    Google Scholar 

  10. Farsiu, S., Robinson, M.D., Elad, M., Milanfar, P.: Fast and robust multiframe super resolution. IEEE Trans. Image Processing 13(10), 1327–1344 (2004)

    Google Scholar 

  11. Freeman, W.T., Jones, T.R., Pasztor, E.C.: Example-based super-resolution. IEEE Computer Graphics and Applications 22(2), 56–65 (2002)

    Article  Google Scholar 

  12. How, H.S., Andrews, H.C.: Cubic spline for image interpolation and digital filtering. IEEE Trans. Acoustics, Speech and Signal Proc. 26(5), 508–517 (1978)

    Google Scholar 

  13. Hung, E.M., de Queiroz, R.L.: Blocking-effect reduction in a reversed-complexity video codec based on a mixed-quality framework. In: Proc. IEEE Int. Telecommun. Symp., pp. 1–6 (September 2010)

    Google Scholar 

  14. Hung, E.M., de Queiroz, R.L., Brandi, F.: Video super-resolution using codebooks derived from key-frames. IEEE Trans. Circuits and Systerms for Video Technology 22(9), 1321–1331 (2012)

    Article  Google Scholar 

  15. Jiang, Z., Wong, T.T., Bao, H.: Practical super-resolution from dynamic video sequences. In: Proc. IEEE Conference on Computer Vision and Pattern Recognition, vol. 2, pp. 549–554. IEEE (2003)

    Google Scholar 

  16. Kawano, H., Suetake, N., Cha, B., Aso, T.: Super-resolution via matching from self-decomposed codebook with local distance measure incorporating pixel correlation. In: Proc. Biennial Conference of the Australian Pattern Recognition Society on Digital Image Computing Techniques and Applications, pp. 317–323 (2007)

    Google Scholar 

  17. Kim, K.I., Kwon, Y.: Single-image super-resolution using sparse regression and natural image prior. IEEE Trans. Pattern Analysis and Machine Intelligence 32(6), 1127–1133 (2010)

    Article  MathSciNet  Google Scholar 

  18. de Oliveira, K.F., Brandi, F., Hung, E.M., de Queiroz, R.L., Mukherjee, D.: Bipredictive video super-resolution using key-frames. In: Proc. SPIE Symposium on Electronic Imaging, Visual Information Processing and Communication (2010)

    Google Scholar 

  19. Protter, M., Elad, M., Takeda, H., Milanfar, P.: Generalizing the nonlocal-means to super-resolution reconstruction. IEEE Trans. Image Processing 18(1), 36–51 (2009)

    Article  MathSciNet  Google Scholar 

  20. Segall, C.A., Katsaggelos, A.K., Molina, R., Mateos, J.: Super-resolution from compressed video. Kluwer International Series in Engineering and Computer Science, pp. 211–242 (2001)

    Google Scholar 

  21. Song, B.C., Jeong, S.C., Choi, Y.: Video super-resolution algorithm using bi-directional overlapped block motion compensation and on-the-fly dictionary training. IEEE Trans. Circuits and Systerms for Video Technology 21(3), 274–285 (2011)

    Article  Google Scholar 

  22. Su, H., Tang, L., Tretter, D., Zhou, J.: A practical and adaptive framework for super-resolution. In: Proc. IEEE International Conference on Image Processing, pp. 1236–1239. IEEE (2008)

    Google Scholar 

  23. Takeda, H., Milanfar, P., Protter, M., Elad, M.: Super-resolution without explicit subpixel motion estimation. IEEE Trans. Image Processing 18(9), 1958–1975 (2009)

    Article  MathSciNet  Google Scholar 

  24. Tappen, M.F., Russell, B.C., Freeman, W.T.: Exploiting the sparse derivative prior for super-resolution and image demosaicing. In: Proc. IEEE Workshop on Statistical and Computational Theories of Vision, Citeseer (2003)

    Google Scholar 

  25. Tom, B.C., Katsaggelos, A.K.: Resolution enhancement of monochrome and color video using motion compensation. IEEE Trans. Image Processing 10(2), 278–287 (2001)

    Article  MATH  Google Scholar 

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Author information

Authors and Affiliations

  1. Tianjin Key Laboratory of Cognitive Computing and Application, School of Computer Science and Technology, Tianjin University, Tianjin, 300072, China

    Yanming Zhu, Kun Li & Jianmin Jiang

  2. School of Electronic Information Engineering, Tianjin University, Tianjin, 300072, China

    Jingyu Yang

Authors
  1. Yanming Zhu
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  2. Kun Li
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  3. Jianmin Jiang
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  4. Jingyu Yang
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Editor information

Editors and Affiliations

  1. EURECOM, Multimedia Department, Sophia Antipolis, France

    Benoit Huet

  2. Department of Computer Science, City University of Hong Kong, Tat Chee Ave, Kowloon, Hong Kong

    Chong-Wah Ngo

  3. Nanjing University of Science and Technology, 210093, Nanjing, China

    Jinhui Tang

  4. Department of Computer Science and Technology, Nanjing University, Xianlin Avenue No. 163, 210023, Nanjing, China

    Zhi-Hua Zhou

  5. School of Computer Science, Carnegie Mellon University, Pittsburgh, PA, USA

    Alexander G. Hauptmann

  6. Department of Electrical and Computer Engineering, National University of Singapore, 4 Engineering Drive 3, 117583, Singapore, Singapore

    Shuicheng Yan

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© 2013 Springer International Publishing Switzerland

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Zhu, Y., Li, K., Jiang, J., Yang, J. (2013). Adaptive Video Super-Resolution Based on Superpixel-Guided Auto-Regressive Model. In: Huet, B., Ngo, CW., Tang, J., Zhou, ZH., Hauptmann, A.G., Yan, S. (eds) Advances in Multimedia Information Processing – PCM 2013. PCM 2013. Lecture Notes in Computer Science, vol 8294. Springer, Cham. https://doi.org/10.1007/978-3-319-03731-8_42

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  • DOI: https://doi.org/10.1007/978-3-319-03731-8_42

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-03730-1

  • Online ISBN: 978-3-319-03731-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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