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Weighted aggregation and fuzzy-concept-guided signal resemblance and expansion for video format conversion

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Abstract

In this paper, we propose an efficient deinterlacing method for HDTV that preserves image structures, edges, and details. In the human visual system, the eyes are more sensitive to high-frequency information such as edge details than low-frequency information such as image background. Therefore, averaging low-pass filter results is not effective for image enhancement. The proposed method is a weighted filtering approach that generates a half-pixel 9-by-9 edge-based line average window. We also propose pixel-resemblance- and pixel-expansion-based fuzzy weights, which are assigned using a triangular membership function. Compared to conventional format conversion methods, the proposed method outperforms all benchmarks in terms of both objective and subjective qualities.

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References

  1. Aubertin P, Pierre Langlois JM, Savaria Y (2012) Real-time computation of local neighborhood functions in application-specific instruction-set processors. IEEE Transactions on Very Large Scale Integration (VLSI) Systems 20(11):2031–2043

    Article  Google Scholar 

  2. Brox P, Baturone I, Sanchez-Solano S, (2008) A motion and edge adaptive interlaced-to-progressive conversion using fuzzy logic-based systems, in Proc. IPMU2008, 1175–1182

  3. Brox P, Baturone I, Sánchez-Solano S, Gutiérrez-Ríos J (2014) "edge-adaptive spatial video de-interlacing algorithms based on fuzzy logic," IEEE Trans. Consum Electron 60(3):375–383

    Article  Google Scholar 

  4. Brox P, Baturone I, Sanchez-Solano S, Gutierrez-Rios J (2014) Edge-adaptive spatial video de-interlacing algorithms based on fuzzy logic. IEEE Trans Consumer Electronics 60(3):375–383

    Article  Google Scholar 

  5. Chen T, Wu HR, Yu ZH (2000) Efficient deinterlacing algorithm using edge-based line average interpolation. Opt Eng 39(8):2101–2105

    Article  Google Scholar 

  6. Chen X, Jeon G, Jeong J (2012) A filter switching interpolation method for deinterlacing. SPIE Optical Engineering 51(10):107402

    Google Scholar 

  7. Doyle T (1998) Interlaced to sequential conversion for EDTV application. in Proc. 2nd Int. Working Processing of HDTV, 412–430

  8. Jakhetiya V, Lin W, Jaiswal SP, Guntuku SC, Au OC (2017) Maximum a posterior and perceptually motivated reconstruction algorithm: a generic framework. IEEE Trans Multimedia 19(1):93–106

    Article  Google Scholar 

  9. Jeon G, Jeong J (2006) Designing Takagi-Sugeno fuzzy model based motion adaptive deinterlacing system. IEEE Trans. Consumer Electronics 52(3):1013–1020

    Article  Google Scholar 

  10. Jeon G, You J, Jeong J (2009) Weighted fuzzy reasoning scheme for interlaced to progressive conversion. IEEE Trans Circuits and Systems for Video Technology 19(6):842–855

    Article  Google Scholar 

  11. Jeon G, Park SJ, Fang Y, Anisetti M, Bellandi V, Damiani E, Jeong J (2009) Specification of efficient block matching scheme for motion estimation in video compression. SPIE Optical Engineering 48(12):127005

    Article  Google Scholar 

  12. Jeon G, Anisetti M, Kim D, Bellandi V, Damiani E, Jeong J (2009) Fuzzy rough sets hybrid scheme for motion and scene complexity adaptive deinterlacing. Image Vis Comput 27(4):425–436

    Article  Google Scholar 

  13. Jeon G, Anisetti M, Bellandi V, Damiani E, Jeong J (2009) Designing of a type-2 fuzzy logic filter for improving edge-preserving restoration of interlaced-to-progressive conversion. Inf Sci 179(13):2194–2207

    Article  Google Scholar 

  14. Jeon G, Anisetti M, Lee J, Bellandi V, Damiani E, Jeong J (2009) Concept of linguistic variable-based fuzzy ensemble approach: application to interlaced HDTV sequences. IEEE Trans Fuzzy Systems 17(6):1245–1258

    Article  Google Scholar 

  15. Kovacevic J, Safranek RJ, Yeh EM (1997) Deinterlacing by successive approximations. IEEE Trans Image Process 6(2):339–344

    Article  Google Scholar 

  16. Kuo CJ, Liao C, Lin CC (1996) Adaptive interpolation technique for scanning rate conversion. IEEE Trans Circuits and Syst Video Technol 6(3):317–321

    Article  Google Scholar 

  17. Lee K, Lee C (2013) High quality spatially registered vertical temporal filtering for deinterlacing. IEEE Trans. Consumer Electronics 59(1):182–190

    Article  Google Scholar 

  18. Mahvash H, Savaria Y, Langlois JMP (2011) Enhanced motion compensated deinterlacing algorithm. IET Image Process 6(8):1041–1048

    Article  MathSciNet  Google Scholar 

  19. Park MK, Kang MG, Nam K, Oh SG (2003) New edge dependent deinterlacing algorithm based on horizontal edge pattern. IEEE Trans Cons Elect 49(4):1508–1512

    Article  Google Scholar 

  20. Park S.-J, Jeon G, Jeong J (2010) Covariance-based adaptive deinterlacing method using edge map, in Proc. Image Processing Theory Tools and Applications (IPTA), 166–171

  21. Park S-J, Jeon G, Wu J, Jeong J (2013) An interpolation scheme based on Bayes classifier. IS&T/SPIE Journal of Electronic Imaging 22(2):023003

    Article  Google Scholar 

  22. Riquelme J, Morillas S, Peris-Fajarnes G, Castro D (2007) Fuzzy metrics application in video spatial deinterlacing. Lect Notes Comput Sci 4578:349–354

    Article  MATH  Google Scholar 

  23. Wang J, Jeon G, Jeong J (2015) A hybrid algorithm using maximum a posteriori for interlaced to progressive scanning format conversion. IEEE/OSA Journal of Display Technology 11(2):183–192

    Article  Google Scholar 

  24. Wu J, Huang J, Jeon G, Cho J, Jeong J, Jiao L (2011) An adaptive autoregressive deinterlacing method. SPIE Optical Engineering 50(5):057001

    Article  Google Scholar 

  25. Wu J, Song Z, Jeon G (2014) GPU-parallel implementation of the edge-directed adaptive intra-field deinterlacing method. IEEE/OSA Journal of Display Technology 10(9):746–753

    Article  Google Scholar 

  26. Wu J, Deng L, Jeo G, et al (2016) GPU-parallel interpolation using the edge-direction based normal vector method for terrain triangular mesh. J Real-Time Image Proc:1–10. doi:10.1007/s11554-016-0575-1

  27. Ying L, Wan X, Jay KC (2002) Introduction to content-based image retrieval-overview of key techniques. In: Castelli V, Bergman D (eds) Image DBs. John Wiley & Sons, Chichester, pp 261–284

    Google Scholar 

  28. Zhang H, Rong M (2014) Deinterlacing algorithm using gradient-guided interpolation and weighted average of directional estimation. IET Image Process 9(6):450–460

    Article  Google Scholar 

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Acknowledgements

This work was supported by the Institutes of Convergence Science and Technology, Incheon National University Research Grant in 2015.

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Authors and Affiliations

  1. Institutes of Convergence Science and Technology, Incheon National University, 12 Gaetbeol-ro, Yeonsu-gu, Incheon, 21999, Republic of Korea

    Yun Joo Chyung, Jee Yon Lee, Sun Young Jung & Pyoung Won Kim

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  1. Yun Joo Chyung
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  2. Jee Yon Lee
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  3. Sun Young Jung
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  4. Pyoung Won Kim
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Correspondence to Pyoung Won Kim.

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Chyung, Y.J., Lee, J.Y., Jung, S.Y. et al. Weighted aggregation and fuzzy-concept-guided signal resemblance and expansion for video format conversion. Multimed Tools Appl 76, 24847–24858 (2017). https://doi.org/10.1007/s11042-017-4641-x

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  • DOI: https://doi.org/10.1007/s11042-017-4641-x

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