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Practical Implementation of Super-Resolution Approach for SD-to-HD Video Up-Conversion

  • Vadim Vashkelis
  • Natalia Trukhina
  • Sandeep Kumar
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6297)

Abstract

Video up-conversion takes significant place in various application areas. One of important application areas is standard-definition (SD) video processing to get high-definition (HD) content for television and broadcast. However, high-quality up-conversion is a challenging task. Most practical implementations use spatial domain processing such as video frame interpolation for video up-scale. Meanwhile, due to sampling limitation the high-frequency component of output HD video cannot be efficiently reconstructed by applying only the spatial domain processing and high-quality up-conversion usually requires temporal domain processing as well. The authors propose practical implementation of such up-conversion technique providing significantly better visual results in comparison to traditional methods of SD to HD up-conversion.

Keywords

Super-resolution video up-conversion SD-to-HD video enhancement 

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References

  1. 1.
    Borman, S., Stevenson, R.: Super-resolution from image sequences - a review. In: Proc. Midwest Symposium on Circuits and Systems (1998)Google Scholar
  2. 2.
    Kim, K.I., Franz, M.O., Scheolkopf, B.: Kernel Hebbian Algorithm for Single-Frame Super-Resolution. In: Proc. Midwest Symposium on Circuits and Systems (1998)Google Scholar
  3. 3.
    Borman, S., Stevenson, R.: Spatial Resolution Enhancement of Low-Resolution Image Sequences - A Comprehensive Review with Directions for Future Research, Department of Electrical Engineering, University of Notre Dame (1998)Google Scholar
  4. 4.
    Farsiu, S., Dirk Robinson, M., (Student Member), Elad, M., Milanfar, P., (Senior Member): Fast and Robust Multiframe Super Resolution (1998)Google Scholar
  5. 5.
    Yuan, S., Abe, M., Taguchi, A., Kawamata, M.: High accuracy wadi image interpolation with local gradient features. In: Proc. of 2005 Int. Symposium on Intelligent Signal Proc. and Comm. Systems, pp. 85–88 (2005)Google Scholar
  6. 6.
    Lukin, A., Kubasov, D.: High-Quality Algorithm for Bayer Pattern Interpolation. Programming and Computer Software 30(6), 347–358 (2004)zbMATHCrossRefMathSciNetGoogle Scholar
  7. 7.
    Li, X., Orchard, M.: New edge-directed interpolation. IEEE Trans. on Image Processing 10(10), 1521–1527 (2001)CrossRefGoogle Scholar
  8. 8.
    Chughtai, M.A., Khattak, N.: An Edge Preserving Locally Adaptive Anti-aliasing Zooming Algorithm with Diffused Interpolation. In: The 3rd Canadian Conference on Computer and Robot Vision (CRV 2006), p. 49 (2006)Google Scholar
  9. 9.
    Rodrigues, L., Borges, D.L., Goncalves, L.M.: A Locally Adaptive Edge-Preserving Algorithm for Image Interpolation. In: Proceedings of the 15th Brazilian Symposium on Computer Graphics and Image Processing, pp. 300–305 (2002)Google Scholar
  10. 10.
    Duchon, C.E.: Lanczos Filtering in One and Two Dimensions. Journal of Applied Meteorology 18(8), 1016–1022 (1979)CrossRefGoogle Scholar
  11. 11.
    Glassner, A.S., Turkowski, K., Gabriel, S.: Filters for Common Resampling Tasks. In: Graphics Gems I, pp. 147–165. Academic Press, London (1990)Google Scholar
  12. 12.
    Barreto, D., Alvarez, L.D., Abad, J.: Motion Estimation Techniques in Super-Resolution Image Reconstruction. In: A Performance Evaluation. Virtual observatory. Plate content digitalization, archive mining and image sequence processing, Sofia, Bulgary, vol. 1, pp. 254–268 (2006)Google Scholar
  13. 13.
    Richardson, E.G.: Iain: H.264 and MPEG-4 Video Compression: Video Coding for Next-generation Multimedia. John Wiley and Sons Ltd, Chichester (2003)CrossRefGoogle Scholar
  14. 14.
    Brown, L.G.: Computing Surveys (CSUR). Columbia Univ., ACM, New York (December 1992)Google Scholar
  15. 15.
    Aptoula, E., Lefevre, S., Ronse, C.: A hit-or-miss transform for multivariate images Source Pattern Recognition Letters, pp. 760–764. Elsevier Science Inc., New York (June 2009)Google Scholar
  16. 16.
    Khosravi, M., Schafer, R.W.: Template matching based on a grayscale hit-or-miss transform. Human Interface Technol. Center, ATT Global Inf. Solutions, Atlanta, GA. IEEE Trans. Image Process. (1996)Google Scholar
  17. 17.
    Perret, B., Lefevre, S., Collet, C.: A robust hit-or-miss transform for template matching applied to very noisy astronomical images. Source, Pattern Recognition 42(11), 2470–2480 (2009)zbMATHCrossRefGoogle Scholar
  18. 18.
    Khosravi, M., Schafer, R.W.: Template Matching Based on a Grayscale Hit-or-Miss Transform. IEEE Transactions on Image Processing 5(6) (June 1996)Google Scholar
  19. 19.
    Tekalp, A.M., Ozkan, M.K., Sezan, M.I.: Highresolution image reconstruction from lower-resolution image sequences and space-varying image restoration. In: ICASSP, San Francisco, vol. III, pp. 169–172 (1992)Google Scholar
  20. 20.
    Chen, T.: Adaptive temporal interpolation using bidirectional motion estimation and compensation. In: IEEE International Conference of Image Processing, pp. 313–316 (2002)Google Scholar
  21. 21.
    Chan, T.-M., Zhang, J., Pu, J., Huang, H.: Neighbor embedding based super-resolution algorithm through edge detection and feature selection. Pattern Recognition Letters 30(5), 494–502 (2009)CrossRefGoogle Scholar
  22. 22.
    Drettakis, G., Scopigno, R.: Visual-Quality Optimizing Super Resolution. Eurographics 27(3) (2008)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Vadim Vashkelis
  • Natalia Trukhina
  • Sandeep Kumar

There are no affiliations available

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